Increased spread of global flash droughts threatens vegetation productivity resilience

Domain: biology
Published: 2026-03-17
Editorial outcome: minor_revision

Abstract
Flash drought (faster-developing drought) has been pervasively intensified, posing detrimental constraints on vegetation productivity. However, the potential expansion of flash droughts from flash drought hotspots to non-hotspots and its associated risks to vegetation productivity remain unknown. Furthermore, the divergence in the underlying drivers governing vegetation productivity responses to flash and slow droughts (slower-developing droughts) is unclear. We quantify the dominant drivers underlying vegetation productivity resilience (the departure of productivity anomalies after drought effect to the long-term mean) to both flash and slow droughts. There exhibits significantly lower productivity resilience to flash drought at flash drought hotspots than non-hotspots (Δ = -0.07 ± 0.02, t = -3.25, p < 0.001). The productivity resilience to flash drought is more sensitive to the same reduction in productivity anomaly and intensified climate stress than slow drought. Carbon dioxide fertilization effect exerts the greatest positive effect on productivity resilience to both flash and slow droughts, although that effect is smaller under flash droughts. Our study underscores the limited global ecosystem resilience to the intensification and spread of flash droughts.

Introduction
Flash droughts are becoming increasingly pervasive accompanied with climate warming and intensification of human activities 1-5. In recent years, rapid intensification of flash droughts has been reported across the United States 6, Europe 7, Sahel 2, China 8 and Australia 9, that significantly impacted agricultural productivity and terrestrial carbon sink 10, 11. The intensification of flash droughts is mainly attributed to complicated interactions of warmer temperature-induced increased water demands 12 and deficit in precipitation supply 1, 13, which increases evapotranspiration 4, 14. Compared with slow droughts, flash droughts are characterized with exceptionally high onset speed and acute depletion of soil water availability within an extremely short time at sub-seasonal timescales 1, 6, 11, 15, 16. Flash droughts have more detrimental effects on vegetation and carbon sequestration than slow droughts 10, 11, 13. However, a comprehensive understanding of the potential divergence in and the underlying drivers for the drought resilience of vegetation productivity to flash and slow droughts at broad scale remains lacking, which are essential for better prediction of the critical consequences of flash drought intensification.

Flash droughts impose pervasively detrimental effects on vegetation productivity across different biomes 10, 11, 13. In response to flash drought, vegetation quickly loses water into the air, thus exacerbating the rapid decline in soil moisture and plant hydraulic function 11, 13. In contrast, slow drought is characterized by a slower soil moisture decline, with relatively mild climate stress 1. A persistent warming and drying trend tends to tightly associate with loss in vegetation resilience 17. Increasing evidences report that vegetation resilience is susceptible to extreme climate events (e.g., extreme drought, heatwaves) and seasonality of water availability 18, 19. Nonetheless, the potential divergence in the resilience of vegetation productivity to flash and slow drought remains unknown, such as higher or lower resilience under flash droughts 20. Additionally, a comprehensive understanding of the potential expansion of flash drought spatiotemporally remains unconclusive, while previous studies mostly focus on the flash drought hotspots 1, 2, where flash droughts prevail. We know even less about the potential expansions of flash droughts from flash drought hotspots to non-hotspots (see Methods), and what impacts on vegetation resilience emerge. We particularly concentrate on the consequences of intensified flash droughts on vegetation productivity resilience, which is central and essential for comprehensive assessment of the detrimental impacts of widespread flash droughts on ecosystem functioning and hence the human well-being.

Here, we map the count, severity, and onset speed of both flash and slow droughts globally from 1950 to 2023. Flash drought hotspots are then identified by integrating all three metrics (see Methods). We investigate the global pattern and underlying drivers of the responses of vegetation productivity to both flash and slow droughts, leveraging on gridded GPP (gross primary productivity) and SIF (sun-induced fluorescence) data and Fluxnet site observations. We further capture the divergence in vegetation productivity responses to flash and slow droughts during the growing seasons by calculating resilience (i.e., the mean of vegetation anomaly after the drought effect) in the period of 2001 – 2019. Different random forest models are applied to evaluate the importance of diverse factors to vegetation productivity resilience. Specifically, the main objectives are to: (1) characterize the spatiotemporal intensification of global flash drought; (2) determine the divergence in vegetation productivity resilience to flash and slow droughts, and (3) identify the drivers underlying vegetation productivity resilience to flash and slow droughts.

Results
Spatiotemporal patterns in flash drought
We first quantified the spatiotemporal patterns in the occurrence of flash and slow droughts from 1950 to 2023. The flash and slow droughts were determined by the decline in onset speed, with flash drought having no less than 5% in percentile for each pentad and slow drought having at least one pentad with the decline rate of less than 5% in percentile (see Methods). The counts, onset speed and severity of flash drought exhibited great spatial heterogeneity. There exhibited a larger number of more severe flash drought events in the western and southern parts of the Amazon, the western Sahel and tropical Southeast Asia (Figure 1a – c). Regions with higher mean annual precipitation and evaporation exhibited greater flash drought counts, higher severity and faster onset speed (Figure 1d – f). Temporally, we observed significant increasing trends in the anomalies of flash drought counts (6.63 ± 0.66 counts yr-1, z = 5.01, p < 0.001), severity (0.01 ± 0.01% yr-1, z = 4.80, p < 0.001) and onset speed (0.02 ± 0.01% / pentad yr-1, z = 5.66, p < 0.001) during 1950 – 2023, with a rapid acceleration occurring after the turning point in the early 21st century (Figure 1g – i). Although the means severity and onset speed of slow drought also increased rapidly (0.01 ± 0.01% yr-1, z = 4.96 and 0.01 ± 0.01% / pentad yr-1, z = 6.84; both p < 0.001) (Figure S1), which were both weaker than flash drought. The ratio of flash droughts to total drought events is accelerating during 1950 – 2023 (0.14 ± 0.09%, z = 6.16, p < 0.001) (Figure S2).

There were notable differences in the anomalies of multiple climate factors at the onset stage between flash and slow droughts. Compared with slow drought, flash drought exhibited significantly greater mean negative anomaly in precipitation (Δ = -0.296 ± 0.004 mm d-1, t = -73.58, p < 0.001). The mean positive anomalies in temperature (Δ = 0.52 ± 0.01 °C d-1, t = 50.97, p < 0.001), radiation (Δ = 5.59 ± 0.07 J m-2 d-1, t = 82.11, p < 0.001), vapor pressure deficit (Δ = 0.63 ± 0.01 hPa d-1, t = 44.55, p < 0.001) and evaporation (Δ = 0.095 ± 0.001 mm d-1, t = 66.84, p < 0.001) were significantly greater during flash drought than during slow drought. Interestingly, flash droughts were associated with a mean higher soil moisture anomaly than slow droughts (Δ = 0.0014 ± 0.0001 m3 m-3 d-1, t = 30.56, p < 0.001) (Figure S3a – f). Consistent patterns in the anomalies of climate factors between flash and slow droughts were also confirmed by Fluxnet observations (Figure S3g – l).

We then identified global hotspots of flash drought based on the magnitude of MFDI (see Methods), which integrates three metrics of the flash drought. A higher magnitude of MFDI over 100 indicates that flash drought events are relatively more frequent, severe, and fast-developed during the onset phase (Figure S4a, b), and thus regions with MFDI higher than 100 can be considered as the flash drought hotspots (Figure S4c). The identified flash drought hotspots mainly encompassed humid regions (i.e., aridity index ≥ 0.65) (99.46%), whereas the non-hotspots comprised 33.58% humid regions and 66.42% non-humid regions (Figure 2a). Flash drought hotspots had significantly higher MFDI (102.29 ± 10.13) than non-hotspots (92.94 ± 15.57) (t = 38.36, p < 0.001) (Figure 2b). Interestingly, there were significantly lower trends in MFDI at flash drought hotspots than non-hotspots (Δ Kendall’s τ = -0.01 ± 0.001, t = -7.07, p < 0.001) from 1950 to 2023 (Figure 2c).

Divergence in vegetation productivity resilience to flash and slow droughts
We quantified and compared the GPP resilience to flash and slow droughts. The GPP resilience is quantified by the difference between mean GPP anomalies within two years after the drought and the multi-year mean growing season GPP. Global GPP showed lower resilience to flash drought than slow drought (Δ = -0.012 ± 0.004, t = -2.74, p < 0.01), and regions with lower GPP resilience to flash drought than that to slow drought are mainly located in North America, central Asia, eastern Europe and central Australia (Figure 3a). The mean GPP resilience to flash drought was significantly higher than that to slow drought (Δ = 0.04 ± 0.02, t = 2.45, p < 0.01) at flash drought hotspots (Figure 3b). However, an opposite pattern was observed in GPP resilience (Δ = -0.05 ± 0.02, t = -2.38, p < 0.01) at non-hotspots (Figure 3b). We further found that the GPP resilience to both flash and slow droughts at flash drought hotspots was significantly lower than that at non-hotspots (Δ = -0.07 ± 0.02, t = -3.25 and Δ = -0.15 ± 0.02, t = -8.80; both p < 0.001) (Figure 3b). Consistent patterns were observed from the SIF data (Figure S5) and verified by Fluxnet site observations (Figure S6).

Drivers of vegetation productivity resilience to flash and slow droughts
The random forest models were applied to investigate the association of vegetation productivity resilience to flash and slow droughts with hydro-meteorological conditions, CO2 fertilization effect (β, defined as ∂Productivity/∂CO2), vegetation response features, plant functional trait, stand features and soil property. In this study, 15 factors were introduced to attribute the vegetation productivity resilience to flash and slow droughts at the pentad scale (see Methods). Partial dependence analysis was performed to quantify the impacts of different factors on vegetation productivity resilience to flash and slow droughts, respectively.

We explored and compared the underlying drivers of GPP resilience to flash and slow droughts. The CO2 fertilization effect was the most important individual factor for GPP resilience to both flash and slow droughts (Figure 4a; Figure 5a). GPP resilience to flash droughts was less sensitive to increasing CO2 fertilization effect than slow drought (Figure 4b; Figure 5b). GPP resilience to flash drought was more sensitive to decrease in the GPP anomalies during and preceding droughts than slow drought at flash drought hotspots, with this pattern fading at non-hotspots (Figure 4c, f; Figure 5c, g). Along with increase in radiation, temperature and evaporation and decrease in the water availability, the GPP resilience to flash drought exhibited a more rapid decline than that to slow drought at flash drought hotspots (Figure 4e, g – k). Climate factors in together have the highest contribution to GPP resilience to both flash and slow droughts at non-hotspots (39.07 ± 3.86% and 34.72 ± 2.64%, respectively). GPP resilience showed more sensitive to intensified climate stress brought by flash drought than slow drought (Figure 5e, f, h, k). The shallower maximum rooting depth was associated with much lower GPP resilience to slow drought than to flash drought at flash drought hotspots (Figure 4d), while GPP resilience showed less sensitive to the increase in maximum rooting depth during flash drought than during slow drought at non-hotspots (Figure 5d). Soil property exerts marginal effects on GPP resilience to both flash and slow droughts. Consistent patterns were observed in the SIF dataset (Figure S7; Figure S8).

Methods
Ecosystem variables
In this study, vegetation productivity was investigated via a daily gross primary productivity (GPP) dataset, which was directly obtained from FluxSat (2001 – 2019) (https://www.earthdata.nasa.gov/data/catalog/ornl-cloud-fluxsat-gpp-fpar-1835-2) 43. We also used 4-day solar-induced chlorophyll fluorescence (SIF) data from the CSIF dataset (2001 – 2019) (https://figshare.com/articles/dataset/CSIF/6387494) for supplementary analyses 44. FluxSat data was derived from the MODIS using the nadir bidirectional reflectance distribution function production (MCD43C4) as input to neural networks that were used to upscale the GPP estimated at flux sites 43. FluxSat dataset has considered the differences in CO2 absorption efficiency between C3 and C4 vegetation 43. Specifically, vegetation was separated into two classes using satellite measurements: (1) highly productive (e.g., in crop regions such as C4 maize with high relative annual maximum GPP values) and (2) all others (e.g., C3 vegetation) 43. FluxSat dataset has been widely used for vegetation response to drought in previous studies 49, 50. The native spatial resolution of the FluxSat GPP data is 0.05 degree. The CSIF dataset is produced based on SIF retrievals from OCO-2 and surface reflectance data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites (0.05 degrees) 44. We used the simple average method to scale the raw data (GPP and SIF) up to 1 degree for matching the gridded climate data. We also linearly interpolated the SIF data (4-days) to the daily scale and then aggregated the daily GPP and SIF data to 5-day (pentad) mean data after deseasonalization and detrending. We also applied Fluxnet site level flux observation dataset (FLUXNET2015, https://fluxnet.org/data/fluxnet2015-dataset/) 45 to further validate our findings. We discarded sites without recorded flash drought and slow drought events. The vegetation type was determined from the international geosphere biosphere program classification in MCD12C1 (https://www.earthdata.nasa.gov/data) 51. In the upscaling process, we used the mode value in the upscaling window to express the vegetation type.

Stand features and plant trait, including the tree density, canopy height and maximum rooting depth, are physiologically associated with plant water transport and use. The tree density determines the intensity of competition in forests and was derived from a global tree density map (https://elischolar.library.yale.edu/yale_fes_data/1/) 52. The canopy height is closely related to vegetation productivity, and was derived from the global 1-km canopy height map dataset (https://lidar.jpl.nasa.gov/data/general.php) 53. The maximum rooting depth affects vegetation productivity resilience to environmental stress and the dataset was derived from the Global Earth Observation for Integrated Water Resource Assessment platform (https://cordis.europa.eu/project/id/603608) 47.

Climate and soil property data
Daily gridded climate data from 1950 – 2023 were obtained from the ERA5_Land dataset of Copernicus (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview) 54, including the air temperature at 2 m above the global surface, downward shortwave solar radiation, total precipitation, evaporation, soil moisture (0 – 7 cm, 7 – 28 cm, and 28 – 100 cm), and 2-m dewpoint temperature. These daily climate data exhibit a spatial resolution of 0.1 degree, which we scaled up to 1 degree (by using the simple average method). On the basis of the ERA5_Land climate dataset, we calculated the vapor pressure deficit 55. We additionally obtained daily soil moisture (0 – 1 m) data from the GLDAS_CLSM dataset (resampled 0.25 degree to 1 degree) 56. The snow water equivalent derived from the GLDAS_CLSM dataset was introduced in our study (https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS) 56. We compiled simulated daily soil moisture (0 – 1 m) data from nine earth system models, which are available in CMIP6 project (https://aims2.llnl.gov/search/cmip6/) 57, to analyze future flash drought pattern. Firstly, we chose the SSP245 scenario which is the most likely scenario to occur in the future 28. Secondly, all the models are with appropriate spatio-temporal resolution for the future analysis. Thirdly, we applied the nearest neighbor method to resample the original simulations into 1 degree and took the 5-day soil moisture mean to capture future flash and slow droughts. Fourthly, we evaluated the performance in soil moisture simulation of flash drought counts across the nine models by Taylor plot (Figure S15). On the premise of ensuring high correlation, we removed the model with lower levels of standard deviation and root mean square deviation (CMCC_CM2_SR5). Finally, we used simulation data of eight earth system models (mrso; r1i1p1f1; model names: ACCESS-CM2, BCC-CSM2-MR, MIROC6, MPPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM and NorESM2-MM) of the CMIP6 under the SSP245 scenario (Table S3). Daily atmospheric carbon dioxide concentration observation dataset was obtained from Global Monitoring Laboratory platform (https://gml.noaa.gov/ccgg/trends/).

The soil cation exchange capacity indicate the soil capacity for nutrient retention. It was obtained directly from the Regridded Harmonized World Soil Database v.1.2 (https://gaez.fao.org/pages/hwsd) 58.

Identification of flash and slow droughts
We defined flash and slow droughts on the basis of soil moisture changes 1. To capture grid-level flash and slow droughts accurately, we used two distinct soil moisture datasets (ERA5_Land and GLDAS_CLSM). The depth-weighted average was calculated for the ERA5_Land soil moisture data (0 – 7 cm, 7 – 28 cm, and 28 – 100 cm) to get 1-m soil moisture 1. The depth-weighted average soil moisture was calculated using Equation 1:
SM = (0.07×SM1 + 0.21×SM2 + 0.72×SM3) (1)
SM indicated the weighted mean soil moisture. SM1, SM2 and SM3 indicated soil moisture of 0 – 7 cm, 7 – 28 cm and 28 – 100 cm, respectively. GLDAS_CLSM soil moisture data based on the global land surface model for the top 1 m depth were also used. We then calculated the mean values of the two datasets to reduce the potential bias. The mean soil moisture data were applied in the following analyses. In each grid pixel, the soil moisture was aggregated into a 5-day average (pentad), which was then converted into percentiles. We further identified flash and slow droughts during the growing seasons in the period of 1950 – 2023 (Figure S16). The flash drought was identified by four rules: (1) the pentad-mean soil moisture decreases from above the 40th percentile to the 20th percentile, with the decline rate of no less than 5% in percentile for each pentad; (2) once the soil moisture drops below the 20th percentile, the flash drought onset ends if the decline rate of a pentad is less than 5% in percentile; (3) if the soil moisture rises above the 20th percentile again, the flash drought terminates; and (4) flash drought lasted at least 4 pentads. Slow drought events were identified as follows: (1) the pentad-mean soil moisture decreases from above the 40th percentile to the 20th percentile, with at least one pentad having the decline rate of less than 5% in percentile; (2) when the soil moisture drops below the 20th percentile, the slow drought onset ends if the soil moisture starts to increase; (3) if the soil moisture rises above the 20th percentile again, the slow drought terminates; and (4) slow drought lasted at least 4 pentads.

Flash and slow droughts differ in the onset speed, of which flash drought exhibits the decline rate of no less than 5% in percentile for each pentad at the onset stage 1. The onset speed was defined as the mean decline rate of the pentad-mean soil moisture percentile at the onset stage, which was the difference between the 40th percentile and the lowest percentile at the onset stage divided by the length of onset 1. The severity was defined as the difference between the 40th percentile and the lowest percentile at the onset stage 25. The flash drought ratio was calculated as the ratio of the flash drought counts to the sum of flash and slow droughts from 1950 to 2023 1. We used the Bayesian change point detection (BEAST) model to automatically detect abrupt turning points of the interannual variation in flash and slow droughts 59.

We also introduced the land-atmosphere coupling index (CSI_SM-VPD) 60, 61. This index can be expressed as follows:
CSI_SM-VPD = corr(SM, VPD) × σ(VPD) (2)
where corr(SM, VPD) is the Pearson correlation coefficient between the pentad-scale soil moisture and vapor pressure deficit during the drought, σ(VPD) is the standard deviation of vapor pressure deficit, and a smaller CSI means a stronger land-atmosphere coupling state.

Division of flash drought hotspots and non-hotspots
We optimized the method to identify flash drought hotspots and non-hotspots on the basis of previous studies 2, 5. We first defined energy-limited and water-limited regions based on the basis of the ecosystem limitation index (Figure S17a). Ecosystem limitation index was calculated by subtracting the correlation between soil moisture and evapotranspiration from the correlation between the temperature and evapotranspiration 62. An ecosystem limitation index value greater than 0 indicates a water-limited region, and an index value less than 0 indicates an energy-limited region. We then used the aridity index to divide the study regions into four large climate regions 48, including arid (aridity index < 0.2), semi-arid (0.2 ≤ aridity index < 0.5), sub-humid (0.5 ≤ aridity index < 0.65) and humid (aridity index ≥ 0.65) regions (Figure S17b). Next, we divided our study region into forest, shrub, crop and grass regions based on the vegetation type map (Figure S17c). Finally, we took the spatial intersection among the patterns of the above three global divisions into 29 regions (e.g., humid forest in energy limited region). Each region is defined by unique vegetation type, moisture and energy conditions, which means these regions exhibit different risks when facing drought stress.

We applied multivariate flash drought indicator (MFDI) to determine the flash drought hotspots 25. The MFDI can be calculated as follows:
Let X = x_ij be the matrix with n rows (representing the number of global grids) and m columns (representing flash drought ratio, counts, severity, and onset speed) and, the standardized matrix, Z = z_ij is then given as:
z_ij = 100 ± ((x_ij - M_xj) / S_xj) × 10 (3)
where Z is transformed using a common scale with mean = 100 and standardized deviation = 10, M_xj and S_xj denote the mean and the standard deviation of the jth indicator given as:
M_xj = (∑_{i=1}^n x_ij) / n (4)
S_xj = √(∑_{i=1}^n (x_ij - M_xj)^2 / n) (5)
Let cv_i be the coefficient of variation for the ith unit:
cv_i = S_zi / M_zi (6)
where M_zi = (∑_{j=1}^m z_ij) / m, S_zi = √(∑_{j=1}^m (z_ij - M_zi)^2 / m)
Then, the generalized form of MFDI is given by:
MFDI = M_zi ± S_zi * cv_i (7)
For each region, we calculated the average value of MFDI and compared it to 100. The regions with the average value of MFDI higher than 100 were defined as flash drought hotspots. Conversely, we defined as non-hotspots.

Responses of vegetation productivity to drought
We quantified the vegetation productivity responses to flash and slow droughts following a conceptual diagram (Figure S18). There are two patterns of post-drought recovery: one growing season recovery and multiple growing season recovery 48. Previous studies have shown that vegetation normally recovers within 2 years after drought disturbance 63, 64. Therefore, we discarded drought events with intervals between two droughts shorter than 2 years. We captured almost exclusively drought events where the vegetation time series showed positive anomaly before the drought’s occurrence and showed negative anomaly during the drought. We only considered the flash drought occurred during growing season and both post-drought recovery patterns of vegetation productivity. The pixel-specific growing season was determined by a pixel-specific threshold of the minimum value plus 30% of the seasonal amplitude fitting for the multiyear averaged GPP 48, 65-68. The growing season identification was further verified with SIF (Figure S19). We investigated vegetation changes within two years after drought cessation to explore the response processes of vegetation productivity to flash and slow droughts. We considered the recovery of vegetation productivity from the maximum negative anomaly point to the multi-year growing season mean state within 2 years to be the recovery process under drought effect. The resilience was quantified as the mean of vegetation anomalies after the drought effect (Y_a) minus the multi-year growing season mean (Y_m) (Equation 8) 69. A resilience value lower than 0 indicates that the effect of the drought event persists after disturbance, with lower values indicating weaker resilience.
Resilience = Y_a - Y_m (8)

Attribution analysis
We established four random forest attribution models for flash and slow droughts at flash drought hotspots and non-hotspots, respectively. In each attribution model, the importances of different factors were evaluated. We considered 15 factors in the attribution analyses, which can be grouped into 6 categories, including climate factors (spatio-temporal), CO2 fertilization effect (spatio-temporal), vegetation response features (spatio-temporal), soil property (spatial), plant trait (spatial) and stand features (spatial). All factors with temporal dimension were deseasonalized and detrended. Multiple climate factors during the drought response period (i.e., the period from the beginning of drought to the maximum vegetation negative anomaly) were considered, including the mean values of temperature, radiation, precipitation, vapor pressure deficit, soil moisture and evaporation (Figure S20). Non-growing season snow anomalies could affect the spring phenology and further alter the post-drought vegetation recovery pattern across growing seasons 70. Thus, we included the non-growing season snow water equivalent. We also considered the mean values of vegetation productivity during the drought response period. Previous studies revealed that the vegetation state during the two months preceding a flash drought could compensate for the vegetation recovery process 39, 71, 72. Therefore, we also introduced the mean GPP of this period into our final random forest models. Soil property (soil cation exchange capacity), plant trait (maximum rooting depth) and stand features (canopy height and tree density) were also included in the final model. Besides, we considered the CO2 fertilization effect (β) 30, which is defined as the relative increase in vegetation productivity in response to the increase in atmospheric CO2 concentration and determined as follows:
β = ∂Productivity/∂CO2 (9)
Many studies have pointed out that the increase of CO2 can promote the plant water use efficiency 29, 73. Therefore, we included water use efficiency into the attribution analyses to assess the indirect impacts of CO2 fertilization effects on vegetation productivity resilience based on Equation 10.
WUE = Productivity / Evapotranspiration (10)
Prior to performing the final attribution models, VIF was used to test the collinearity among factors, and the factors with a larger VIF (VIF > 5) were removed to prevent the confusion effects (Figure S21a) 74.

The random forest model provides the advantages of high prediction accuracy, high robustness, accurate factor interpretation and ease of use 46. Separate random forest models were constructed to investigate the differences in the drivers underlying vegetation productivity resilience to flash and slow droughts at flash drought hotspots and non-hotspots. Here, we defined that each random forest had 300 binary trees with 5 leaves. The R2 value of the random forest attribution models were all above 0.80 (Figure S21b – e). A factor importance ranking was obtained via the permutation importance method based on Out-of-Bag data, with values normalized. The permutation feature importance method is a model inspection technique applicable for any fitted model and is especially useful for nonlinear models. The importance of permutation features is defined as the decrease in model accuracy when a single feature value is randomly perturbed. The larger the decrease after the permutation, the greater the importance of this factor 75. We selected the top ten factors as major factors for further analyses. Partial dependence plot was used to characterize the relationship between each factor and vegetation productivity resilience 76.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript NCOMMS-25_04110 aims to understand the mechanism explaining the divergent responses of GPP to flash vs. slow droughts by quantifying the contributions of various environmental factors to the GPP change under droughts. Global-scale estimations of flash/slow droughts and the GPP resilience between 1950-2023 have been mapped based on 1m-deep reanalysis soil moisture time series. The manuscript is well-organized overall and has a clear structure. The results agree with the previously reported increasing spread of flash droughts and present insights into the understanding and prediction of the ecosystem's response to the changing climate. However, the result section should be written in a way that is more cohesive and concise. Its current form may emphasize reporting the outcomes of data analysis more than backing up the arguments. I also have some questions regarding the methodology and authors' interpretation of their results. Please see my detailed comments below.\n1. Abstract lines 25-27 If increased CO2 strongly drives the vegetation growth resilience to both flash and slow droughts, you should delete the other half of the sentence \"...a smaller effect on vegetation growth resilience to flash drought\" or revise it as something like \"...although that effect is smaller with regard to flash drought compared to slow drought \"\n2. Abstract lines 27-30: What does \"deterioration in vegetation and hydrothermal factor\" mean?\n3. Abstract lines 27-30: Are flash drought non-hotspots the same as slow drought hotspots? What is the response of vegetation growth resilience to slow drought hotspots (decreased or no effect?) and why did CO2 still explain most of this response?\n3. Regarding Figure 5a and key point #2 lines 39-41 \"...whereas the effect on vegetation growth resilience to flash drought was smaller\". Are relative contributions of drivers comparable between RF models? Did you specify the leaf node of each tree, the bootstrapping and the shuffle procedures to make the structure of RF models identical? The relative importance values can be compared within a model, but not across models, to give the relative rank of various drivers. A larger relative importance of a specific driver in the slow drought model than that in the flash drought model may not necessarily indicate the effect of that driver on the GPP resilience to slow drought is smaller than in the case of slow drought.\n4. Key point#3: Why \"climate factors provided the highest explanatory power at non-hotspots\"? Maybe briefly touch on the mechanisms?\n5. Figure 1: Please make sure the units and legend color are consistent for each variable across panels. a and d have different color schemes (yellow-red vs. blue-red) for the drought counts. b and e have different units (90 vs. 0.9) for the drought ratio. c and f have different legend text (\"%/pentad\" vs \"Onset speed (%/pentad)\") for the onset speed.\n6. Lines 124-126: Why is soil moisture considered as one of the climate factors in Figure S3?\n7. Line 141: Maybe the title could be \"Divergence in... responses to flash and slow droughts\" since both resilience and resistance were covered in this subsection.\n8. lines 401-403: Since you are using GPP to represent the vegetation growth, maybe it's more appropriate to use \"vegetation productivity\" instead of \"vegetation growth\" in the title and related main text because ecologists have traditionally used other proxies to represent vegetation growth and vegetation resilience.\n9. Methods lines 471-473: Why does the onset stage end at the first 5-day (3rd pentad) when the soil moisture is below the 20th percentile for the flash drought, but at the second 5-day (6th pentad) for the slow drought in Fig S12?\n10. Still lines 471-473: But in Fig S12 (a), it's at the end, not the beginning, of the recovery stage (6th pentad) when the soil moisture begins to increase. Suggest deleting \"if the soil moisture level began to increase\"\n11. Lines 478-480: This second rule seems to apply to the slow drought as well.\n12. Still lines 478-480: Suggest replacing \"onset\" with \"decline\".\n13. lines 483-485: Should it be \"the 40th percentile\" or \"the percentile of the last pentad before the onset stage\"?\n14. Lines 516-517: Did you find drought events during which GPP increased?\n15. Lines 517-518: The terms \"one growing season recovery\" and \"multi growing season recovery\" were used in Fig S15, please be consistent.\n16. Lines 518-520: What about droughts that persist and frequently happen? Any long-term lags, propagation, legacy effect, or memory of soil moisture resulted?\n17. Lines 523-524 Please rephrase it and explain which vegetation index was used and what the pixel-specific threshold was used for.\n18. Lines 547-549 Did not find the citation for \"the previous study\".\n19. Lines 556-559 What did you compare the correlation coefficient with? Vegetation resilience? What's the purpose of retaining factors with abs(R) less than 0.7? Also, please add the absolute notation in Fig S16.\n20. Lines 559: Why vpd was not included? The correlation coefficients between vpd and other factors were also not shown in Fig S16a.\n(Remarks on code availability)\nAccess to the MatLab code is available through the private link of CodeOcean. README file is provided with a description for each code script. The figures in the manuscript could be reproduced using the post-processed data the authors uploaded, though the code of data preprocessing (e.g., for producing the aggregated pentad-mean soil moisture) is not available.", "reviewer_2": "Guo et al quantify drought resistance and resilience of vegetation across the globe. They use a range of vegetation remote sensing datasets like CSIF and FluxSat to characterize these responses of vegetation to flash and slow droughts. They report a range of results including the prevalence of increasing frequency of flash droughts, less resilience of vegetation to flash droughts than slow droughts, and strong relation of CO2 to drought resilience as explained by a random forest model. The methods do appear to be mostly careful and I can appreciate the thought the authors put into those, but in general I had trouble following the large breadth of results. I also question the vegetation response differences claimed between flash and slow drought and hotspots and non hotspots. I think the breadth of results covered here in the analysis are actually a weakness in this case. The analysis could be stronger if more honed on fewer of these arguments so we can gain confidence in the claims. In general, I had trouble following the narrative and am left wondering what the takeaways are. It is unclear if these are addressable issues or not but I do think more work is required here. See my comments below as a guide to improve the manuscript.\nMajor comments\n1. There is very little difference between fast and slow drought response in figure 3. In fact the variability around the medians is very large and I am surprised to see that the differences were found to be statistically significant. Potentially, this is an artifact of having many data points. Which significance test was used here? This issue castes some doubt on the differences for flash and non flash droughts and I am not sure we can confidently make many of these claims in general in lines 142-165 and throughout figure 3. At least they don’t seem like strong arguments.\n2. There are many arguments being made throughout the study about flash drought changes in time, differences between slow and fast drought, differences between hotspots and non hotspots, and then the many factors explaining the differences in patterns. I’ll admit I really struggled to follow the story throughout following these different threads. Potentially, one aspect that can help is to merge the results and discussions sections more because so much material is covered in both. It requires a lot of bouncing back and forth between the figures, results text, and discussion text. Another point that can help is to trim out the arguments that aren’t contributing much and focus more on the messages that do.\n3. I am suspicious about why there are so many flash droughts in humid locations and so few in dry locations. I in general agree with how the droughts and flash droughts are identified with the different criteria starting in line 469. However, I am unsure about the 5%/pentad definition for flash drought. This should be a relative change and not a magnitude change. Otherwise, dry locations will rarely have high enough soil moisture to lose moisture at this rate. It should be based on a relative change to normalize across different conditions.\n4. I am not sure what we are learning from the attribution analysis in Figures 4 and 5. I am admittedly having trouble articulating how I am not following everything but will give a few examples here. First of all, it is unclear whether relationships are being evaluated spatially or temporally and therefore I am not sure how to interpret whether a variable “controls” vegetation drought resilience (as describing spatial gradients, or describing how they respond in time). Second, the perceived processes governing how the variables are related are a bit unclear. The CO2 concentration itself is not considered a fast-acting variable that can directly change a plant response to drought. Effectively, all plants should be experiencing the same mean levels of CO2 because they are well mixed, so I am guessing this analysis is being conducted temporally? Otherwise, water use efficiency or a stomatal trait is potentially more suited in place of CO2. This could be an issue of timescales where CO2 variations have such strong trends compared to the other variables that they are highly related to trends in the drought metrics. As a counterarguement to show my point: if the analysis is conducted over 1-5 years, CO2 may no longer be a large explanatory variable. Finally, the remainder of the variables are so close in their explanatory value that I am not quite clear what we have learned from this analysis. The differences in hotspots and slow and fast drought are so small as well that I don’t think there is much information presented other than we can’t distinguish much from what the attribution analysis is showing.\nMore specific comments\nL23-25: at this point, quantities in the parentheses are not well defined. Suggest clarifying or removing\nL92: it would be worth giving context early how flash drought and slow drought differ\nL126: again, a definition of what a flash drought hotspot is would be very helpful. It also feels a bit strange to have this seemingly main result all be in the SI\nL153: are we just looking at differences of drought response in difference climatic regions?\nL157: a definition of resilience and resistance would be helpful\nL168-169: in time? In space?\nL172: I don’t understand this statement. Is this increasing co2 in time or co2 time variability? It is unclear if these are relationships in time, space, or both\nL224-226: the lack of definitions, such as how hotspots are defined, in the main text makes it challenging to follow this discussion\nL248-250: maybe the more direct variable acting through co2 is water use efficiency or stomatal strategies (including c3 versus c4)? I am generally struggling to follow how co2 is such a strong factor explaining drought strategies\nL392-394: I am not seeing where this result is represented in figure 4\nL477: is this 5% by magnitude of soil moisture or a 5% relative change? This makes a big difference and might explain why so few dry locations see flash droughts based on this definition. Soil moisture is already low and near a lower bound and thus is unable to drop 5% by magnitude.\nL549-553: are these spatial and temporal changes of these variables? Or just temporal?\nFigure 3: the differences reported here are very small. Which significance tests were used?\nFigure 4: there aren’t too large of differences between drought and non drought hotspots and I am unclear what to takeaway from this other than conclusions about CO2.\n(Remarks on code availability)", "reviewer_3": "MANUSCRIPT REVIEW for \"Increased Spread of Global Flash Drought Threats to Vegetation Growth Resilience\"\nThe paper, by Guo et al., demonstrates good technical proficiency in data analysis, effectively handling multiple datasets to explore flash drought characteristics. However, it falls short in critical areas: the scientific rationale and mechanisms driving the results are largely absent, and the motivation lacks clarity and robustness. Given the significant gaps in scientific justification and contribution, I recommend REJECTION, as these issues undermine the paper’s overall impact and validity.\nMajor Comments:\nThe manuscript is difficult to follow, and the flow of the discussion is lacking. The integration of slow and flash droughts is unclear, making it hard to understand the connection between the two. Additionally, the novelty of the work is not well-established, leaving the importance of the study uncertain. Despite citing 90 articles, the manuscript fails to clearly explain the unique contribution of the research. Many of the objectives addressed in the study have already been covered in the cited papers. Furthermore, the discussion of CMIP6 (line 115) seems out of place, as there is no prior explanation of which climate models were selected, how they were chosen, or the methods used for bias correction and downscaling. Additionally, the process of deriving flash droughts (FDs) is not adequately addressed.\nThe authors have conducted a solid data analysis, demonstrating technical proficiency in their approach. However, a significant limitation undermining the paper’s clarity is the absence of justifications or mechanisms explaining the results. While the methods appear sound, the paper focuses solely on data analysis without exploring the underlying processes driving flash drought characteristics. For instance, the claim that energy-limited regions exhibit the highest FD ratio, decay, and onset rates is presented without subsequent elaboration on why this occurs, particularly in humid regions, and why transitional regions differ. To strengthen the study, I recommend that the authors delve into the scientific rationale—for example, linking findings to physical drivers like evaporative demand, soil-vegetation feedbacks, or regional climate dynamics—in the discussion.\nThe authors introduce the term \"slow drought,\" but its meaning remains unclear. While droughts are generally understood as slow or creeping phenomena, the paper does not specify whether \"slow drought\" refers to meteorological, hydrological, or soil moisture drought—or if it encompasses all types. When introducing new terminology, particularly in the abstract or introduction, greater care is needed to provide a precise definition. I recommend that the authors clearly define \"slow drought\" early in the manuscript to avoid ambiguity and strengthen its scientific grounding.\nThe motivation and research objectives of the paper are not strong. The introduction starts well by acknowledging previous flash drought work, but it doesn’t discuss mechanisms. Suddenly, the authors bring up the divergence of slow and flash droughts without properly justifying the research gap. The paper would benefit from a more methodical discussion of why this study matters. Based on the introduction, I didn’t find the analysis or research objectives to be clear. A stronger justification for the study’s purpose and contribution would enhance its relevance. I suggest the authors provide a clearer explanation of the significance of their work.\nThe statement at Line 97, claiming that “energy-limited regions show a significantly high FD ratio,” does not hold for the Eastern USA, a major energy-limited region. Here, the flash drought (FD) ratio ranges from 10% to 15%, which is notably low rather than high. This contradiction undermines the generalization and requires clarification or revision to reflect regional variability accurately. Can the authors explain why this is happening?\nA significant drawback of this paper is the heavy reliance on results presented in the supplementary file, with approximately 70% of the discussion referencing supplementary figures. Supplementary material should support, not dominate, the main text by providing additional context for key arguments. Requiring readers to frequently consult the supplementary file disrupts the flow and accessibility of the narrative. I strongly recommend integrating critical supplementary figures into the main manuscript and discussing them there to improve readability and coherence.\nThe definition of flash drought (FD) hotspots is flawed and requires revision. The authors designate FD hotspots as regions where median values of FD metrics (onset, decay, and FD ratio) exceed the global median, inherently classifying roughly 50% of regions as hotspots (e.g., 46% of global land area in their results). This statistical approach is problematic, as it dilutes the concept of a “hotspot” by encompassing nearly half the globe, rather than pinpointing areas with exceptional FD impact. Hotspots should reflect physically meaningful regions with heightened FD severity—e.g., specific areas like X1, X2, or X3, identified by onset, decay, and intensification rates—rather than a vague percentage of land area. I suggest redefining hotspots based on absolute impact criteria and tying them to distinct geographic regions for clarity and relevance.\nThe authors utilize multiple predictors, including precipitation, radiation, soil moisture, and available water holding capacity (AWC). I suggest reconsidering the inclusion of AWC, as soil moisture already encapsulates sufficient information about water availability and storage capacity. Incorporating AWC as a separate predictor may introduce redundancy, and its exclusion could streamline the analysis without compromising the results. I recommend justifying its use or removing it to enhance model efficiency.\nThe font sizes in the figures can be increased for better readability.\nIn Figure 3, I don’t notice anything significantly different between the hotspot vs. non-hotspot regions. However, the authors state that the results are significantly different across these regions. All the plots in Figure 3 look similar to me. Did the authors perform any statistical significance testing?\nMinor Comments:\nThe title is a little difficult to understand since the phrasing “threats vegetation growth resilience” is used as a verb. I have an alternative suggestion: “Increased Spread of Global Flash Drought Poses a Threat to Vegetation Growth Resilience” or “Increased Spread of Global Flash Droughts Threatens Vegetation Growth Resilience.”\nKeywords: \"Slow drought\" is not a key term. Please change it to “drought.”\nKey points (2) and (3) are very large. Please check the allowed maximum length of characters for the key words for Nature Communications. It’s better to split them into two separate key points.\nReference (4) by Satoh et al. is not a flash drought paper but rather a hydrological drought paper. Hence, remove it.\nLine 98: When you say “significantly greater,” did you assess this using a statistical significance test, or was it just visually interpreted from the figure?\nFigure 1(a, d, g): Instead of writing “times,” simply write “FD counts.” \"Times\" is not an interpretable word. Be consistent with the figure captions.\nLine 449: What do you mean by “cation?”\nHow did you handle the ERA5 soil moisture data, which are provided at 3 depths? Did you take a weighted average?\n(Remarks on code availability)"}, "author_response": "Point-to-point responses to reviewers’ comments\nReviewer #1\nThe manuscript NCOMMS-25_04110 aims to understand the mechanism explaining the\ndivergent responses of GPP to flash vs. slow droughts by quantifying the contributions of\nvarious environmental factors to the GPP change under droughts. Global-scale estimations\nof flash/slow droughts and the GPP resilience between 1950 and 2023 have been mapped\nbased on 1m-deep reanalysis soil moisture time series. The manuscript is well-organized\noverall and has a clear structure. The results agree with the previously reported increasing\nspread of flash droughts and present insights into the understanding and prediction of the\necosystem's response to the changing climate. However, the result section should be\nwritten in a way that is more cohesive and concise. Its current form may emphasize\nreporting the outcomes of data analysis more than backing up the arguments. I also have\nsome questions regarding the methodology and authors' interpretation of their results.\nPlease see my detailed comments below.\nREPLY: Thank you for your insightful comments and constructive suggestions.\nWe acknowledged the issues you have raised, and they highlighted gaps in our\nexplanations and drawbacks in the method presentation.\nWe thoroughly revised the results and methods sections, strengthened the logical flow, and\ntightened the link between results and arguments, by incorporating all of your comments\nand suggestions. We hope we have addressed the issues of clarity and readability, making\nthe manuscript easy to understand.\n1. Abstract lines 25-27 If increased CO strongly drives the vegetation growth resilience to\n2\nboth flash and slow droughts, you should delete the other half of the sentence “...a smaller\neffect on vegetation growth resilience to flash drought” or revise it as something like\n“...although that effect is smaller with regard to flash drought compared to slow drought”.\nREPLY: We have revised this sentence into “Carbon dioxide fertilization effect exerted\nthe greatest positive effect on productivity resilience to both flash and slow\ndroughts, although that effect was smaller under flash droughts.” according to your\nsuggestion (Lines 29-31).\n2. Abstract lines 27-30: What does “deterioration in vegetation and hydrothermal factor”\nmean?\nREPLY: We intended to state that compared with slow drought, the vegetation productivity\nresilience exhibited more sensitive to the reduction in vegetation productivity anomaly and\nthe intensification of climate stress caused by flash drought at flash drought hotspots.\nOur former presentation is misleading. We have accordingly revised this sentence into\n“The productivity resilience to flash drought was more sensitive to reduction in\nproductivity anomaly and intensified climate stress than slow drought at flash\ndrought hotspots.” (Lines 31-33).\n3. Abstract lines 27-30: Are flash drought non-hotspots the same as slow drought hotspots?\nWhat is the response of vegetation growth resilience to slow drought hotspots (decreased\nor no effect?) and why did CO still explain most of this response?\n(1) Are flash drought non-hotspots the same as slow drought hotspots?\nNo, they are not the same. Here, we identified the hotspots of slow drought using the MSDI\n(multivariate slow drought indicator), which is similar to MFDI (multivariate flash drought\nindicator, please see the detailed illustration in Lines 471-489 in our revision) (in detail see,\nMukherjee et al., 2022).\nThe spatial pattern in flash drought non-hotspots and slow drought hotspots was shown in\nFigure A1. We found the slow drought hotspots is different to flash drought non-hotspots,\nand cover a much smaller range than flash drought non-hotspots (Figure A1).\nhotspots. (a) Spatial pattern in slow drought hotspots. (b) Spatial pattern in flash drought non-\nhotspots.\n(2) What is the response of vegetation growth resilience to slow drought hotspots\n(decreased or no effect?)\nWe compared the GPP responses to both flash and slow drought events at slow drought\nhotspots. There were 92.93% and 93.92% grids showing negative anomalies in GPP when\nfacing flash and slow drought at slow drought hotspots. GPP reacts faster and has lower\nresistance to flash droughts, but shows lower resilience to slow droughts (Figure A2).\nFigure A2. Difference in GPP responses to flash and slow droughts in slow drought\nhotspots. (a) The differences in the GPP (gross primary productivity) resilience to flash\nand slow droughts. (b) The differences in the responses of GPP to flash and slow droughts,\nincluding lag time, resistance and resilience. Error bars in (a) and (b) indicate the 95%\nconfidence intervals of the mean lag time, resistance and resilience. Different capital letters\nindicate statistical significance (p < 0.05) in GPP resilience between flash and slow\ndroughts by one-tailed Student’s t-test.\n(3) Why did CO still explain most of this response?\n2\ndrought hotspots (Figure A3). We performed a random forest model for evaluating the\nattribution of diverse factors to the GPP resilience to flash and slow drought events at slow\ndrought hotspots, following the same protocol as that for flash drought hotspots and non-\nhotspots.\n2\net al., 2017), carboxylation efficiency (Lu et al., 2020), and plant enzyme activity (Sophie\net al., 2025; Lambers et al., 2019), thus improving photosynthesis efficiency (Chen et al.,\n2024). They also decrease plant transpiration, enabling plants to retain more water under\nthe same soil moisture conditions or utilize limited soil moisture more effectively in droughts\n(Li et al., 2023). These processes could help plants maintain physiological functions and\nuse more water for growth and tissue repair after drought stress (Soh et al., 2019). Our\nanalyses highlighted the important role of CO fertilization effects on the productivity\n2\nresilience to both flash and slow droughts.\nNotably, this result was not included in the manuscript to make the manuscript more\nconcise and readable, given we focused on the spread of flash drought and its crucial\nimpacts on vegetation productivity resilience.\nFigure A3. The importance of factors for GPP resilience to flash and slow droughts\nat slow drought hotspots. The importance of diverse factors for the GPP (gross primary\nproductivity) resilience to flash and slow droughts at flash drought hotspots derived from\nthe random forest model, including β (carbon dioxide fertilization effect), mean GPP during\ndrought response period, mean temperature during drought response period, canopy\nheight, mean radiation during drought response period, mean evaporation during drought\nresponse period, mean vapor pressure deficit during drought response period, soil cation\nexchange capacity, maximum rooting depth, mean soil moisture during drought response\nperiod, mean precipitation during drought response period, preceding GPP (mean GPP in\ntwo months preceding drought), non-growing season snow water equivalent, mean water\nuse efficiency during drought response period, and tree density. The red and blue points\nindicate factor importance for flash and slow droughts at slow drought hotspots,\nrespectively.\nReference:\nMukherjee, S. & Mishra, A. K. A multivariate flash drought indicator for identifying global hotspots\nand associated climate controls. Geophys. Res. Lett. 49, e2021GL096804 (2022).\nPiao, S., et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1,\n5\n14-27 (2020).\nChen, T., et al. Elevated atmospheric CO concentration and vegetation structural changes\n2\ncontributed to gross primary productivity increase more than climate and forest cover changes in\nsubtropical forests of China. Biogeosciences 21, 2253-2272 (2024).\nLiang, X., et al. Stomatal responses of terrestrial plants to global change. Nat. Commun. 14, 2188\n(2023).\nKeeling, R., et al. Atmospheric evidence for a global secular increase in carbon isotopic\ndiscrimination of land photosynthesis. P. Natl. Acad. Sci. 114, 10361-10366 (2017).\nLu, X., et al. Maximum Carboxylation Rate Estimation With Chlorophyll Content as a Proxy of\nRubisco Content. J. Geophys. Res.: Biogeo. 125, e2020JG005748 (2020).\nSophie, A. Z., et al. Centennial-scale atmospheric CO rise increased photosynthetic efficiency in a\n2\ntropical tree species. New Phytol. 246, 131-143 (2025).\nLambers, W. K., et al. Photosynthesis, Respiration, and Long-Distance Transport: Photosynthesis.\nPlant Physiol. Eco.. 1, 11-114 (2019).\nLi, F., et al. Global water use efficiency saturation due to increased vapor pressure deficit. Science\n381, 672-677 (2023).\nSoh, W. K., et al. Rising CO drives divergence in water use efficiency of evergreen and deciduous\n2\nplants. Sci. Adv. 5, eaax7906 (2019).\n4. Regarding Figure 5a and key point #2 lines 39-41 \"...whereas the effect on vegetation\ngrowth resilience to flash drought was smaller\". Are relative contributions of drivers\ncomparable between RF models? Did you specify the leaf node of each tree, the\nbootstrapping and the shuffle procedures to make the structure of RF models identical?\nThe relative importance values can be compared within a model, but not across models,\nto give the relative rank of various drivers. A larger relative importance of a specific driver\nin the slow drought model than that in the flash drought model may not necessarily indicate\nthe effect of that driver on the GPP resilience to flash drought is smaller than in the case\nof slow drought.\nREPLY: Thank you for your insightful comments. We try to answer your questions one by\none.\n(1) Are relative contributions of drivers comparable between RF models?\nWe agree with you that the relative contributions of different drivers from different random\nforest models may not be directly comparable, as these values are typically interpreted\nwithin the context of a single model. To enable cross-model comparison and provide a\nmore robust assessment, we have adopted the Permutation Importance method based on\nOut-of-Bag (OOB) data. During random forest construction, each tree is trained on\nbootstrap samples, with unselected samples serving as OOB samples. For each feature,\nits value in the OOB sample is randomly shuffled to calculate the shuffled OOB error. The\nfeature's importance score is the difference between the shuffled and original OOB errors.\nA larger difference indicates a greater influence on model prediction performance. OOB\nerror values offer a unified way to estimate each feature's contribution and can be averaged\n6\nfor global importance values. They allow reliable cross-model comparisons as absolute\nindicators (Altmann et al., 2010).\nWe re-performed our analyses using this method. We accordingly revised the methods and\nresults. The absolute importance of different factors in different models was shown in\nFigure A4.\nWe copied our revision here for your convenience, “A factor importance ranking was\nobtained via the permutation importance method based on Out-of-Bag data. The\npermutation feature importance method is a model inspection technique applicable\nfor any fitted model and is especially useful for nonlinear models. The importance\nof permutation features is defined as the decrease in model accuracy when a single\nfeature value is randomly perturbed. The larger the decrease after the permutation,\nthe greater the importance of this factor (Altmann et al., 2010).” (Lines 560-565).\nFigure A4. Factor importance across all attribution models. The factors included mean\ntemperature during drought response period, mean radiation during drought response\nperiod, mean precipitation during drought response period, mean evaporation during\ndrought response period, mean soil moisture during drought response period, mean vapor\npressure deficit during drought response period, non-growing season snow water\nequivalent, β (carbon dioxide fertilization effect), mean water use efficiency during drought\nresponse period, preceding GPP (mean GPP in two months preceding drought), mean\nGPP during drought response period, soil cation exchange capacity, canopy height,\nmaximum rooting depth and tree density. The darker colors indicate higher importance.\n(2) Did you specify the leaf node of each tree, the bootstrapping and the shuffle\nprocedures to make the structure of RF models identical?\nYes, we adopted the same parameters (5 leaves and 300 trees) for all the flash and slow\ndrought random forest attribution models. These parameters were determined by the\nexperiments of loss curves (Figure A5).\nWe included this information into method section in the revision “Here, we defined that\n7\neach random forest had 300 binary trees with one covariate chosen at random from\nthe full set to determine the splitting rule, with a minimal terminal node size of 5.”\n(Lines 557-559).\nFigure A5. Parameter selection in different models. (a) Flash drought in flash drought\nhotspots. (b) Slow drought in flash drought hotspots. (c) Flash drought in non-hotspots. (d)\nSlow drought in non-hotspots. The red loss curves indicate the flash drought model, and\nthe blue loss curves indicate the slow drought model.\n(3) How to indicate the effect of that driver on the GPP resilience to flash drought is\nsmaller than in the case of slow drought?\nOur former presentation is misleading. We intend to state that the GPP resilience was less\nsensitive to increasing CO fertilization effects during flash drought than slow drought at\n2\nboth flash drought hotspots and non-hotspots (in detail see Figure A6, with less steeper\nresponse curves for flash droughts).\nIn our revision, we accordingly revised these presentations (Lines 163-164).\nFigure A6. The partial dependency plots of CO fertilization effect on GPP resilience\n2\n8\nto flash and slow droughts. (a) Partial dependency plots of CO fertilization effects (β)\n2\non GPP resilience to flash droughts at flash drought hotspots (red) and non-hotspots (blue).\n(b) Partial dependency plots of CO fertilization effects on GPP resilience to flash (red) and\n2\nslow (blue) droughts at flash drought hotspots. (c) Partial dependency plots of CO\n2\nfertilization effects on GPP resilience to flash (red) and slow (blue) droughts at non-\nhotspots. \nReference:\nAltmann, A., Toloşi, L., Sander, O. & Lengauer, T. Permutation importance: a corrected feature\nimportance measure. Bioinformatics 26, 1340-1347 (2010).\n5. Key point#3: Why “climate factors provided the highest explanatory power at non-\nhotspots”? Maybe briefly touch on the mechanisms?\nREPLY: Relying on the Permutation Importance method, we evaluated the importance of\ndifferent driving factors (also see our reply to your comment #4). Climate factors in together\nhave the highest mean contribution to GPP resilience to both flash and slow droughts at\nnon-hotspots (32.29 ± 2.05% and 30.23 ± 1.49%, respectively). It shows they play the most\nimportant role in shaping GPP resilience during both flash and slow droughts at non-\nhotspots.\nFollowing your valuable suggestion, we have expanded the relevant discussion: “The rise\nin temperature led to increase evaporation and radiation, together with decrease in\nprecipitation (Yuan et al., 2023; Christian et al., 2021; Qing et al., 2022), resulting in\nstomatal closure and decreased photosynthesis (Li et al., 2023; Martin-StPaul et al.,\n2017), which inhibited vegetation productivity resilience (Gu et al., 2025; Li et al.,\n2023; Jiao et al., 2021). At non-hotspots with low background moisture levels,\nvegetation has adopted conservative adaptation strategies in response to persistent\nand mild drought (e.g., slow drought) through long-term adaptation and evolution\n(Li et al., 2023). Therefore, vegetation can sustain long-term slow growth (He et al.,\n2018). However, we found that both occurrence counts and severity of flash drought\nat non-hotspots increased rapidly (Figure 2e in our revision; Figure S4 in our\nsupplement martial). With a warmer temperature and rapider water loss, vegetation\nproductivity resilience to flash drought decreases more instantly than that to slow\ndrought (Figure 5e, f, h, j, k in our revision; Figure S8f – j in our supplement martial)\n(Zhang et al., 2025). Flash drought can cause much rapider consumption of scarce\nsurface water than slow drought (Yuan et al., 2023). This rapid soil drying threatens\nvegetation growth with shallow maximum rooting depth more severely (Liu et al.,\n2023; Fan et al., 2017; Jing et al., 2025). Although vegetation is normally with deeper\nrooting depth at non-hotspots (Table S3 in our revision), the capability of water\nuptake from deeper soils might not satisfy the dramatic water demands of\nvegetation during flash drought at non-hotspots, which are already under high water\nstress (Figure 5d in our revision; Figure S8d in our revision) (Chen et al., 2025).”\n9\n(Lines 266-281).\nReference:\nYuan, X., et al. A global transition to flash droughts under climate change. Science 380, 187-191\n(2023).\nChristian, J. I., et al. Global distribution, trends, and drivers of flash drought occurrence. Nat.\nCommun. 12, 6330 (2021).\nQing, Y., Wang, S., Ancell, B. C. & Yang, Z.-L. Accelerating flash droughts induced by the joint\ninfluence of soil moisture depletion and atmospheric aridity. Nat. Commun. 13, 1139 (2022).\nLi, W., et al. Widespread and complex drought effects on vegetation physiology inferred from space.\nNat. Commun. 14, 4640 (2023).\nMartin-StPaul, N., Delzon, S. & Cochard, H. Plant resistance to drought depends on timely stomatal\nclosure. Ecol. Lett. 20, 1437-1447 (2017).\nGu, L., et al. Flash drought impacts on global ecosystems amplified by extreme heat. Nat. Geosci.\n10.1038/s41561-025-01719-y (2025).\nJiao, W., et al. Observed increasing water constraint on vegetation growth over the last three\ndecades. Nat. Commun. 12, 3777 (2021).\nHe, B., Huang, L., Chen, Z. & Wang, H. Weakening sensitivity of global vegetation to long-term\ndroughts. Sci. China Earth Sci. 61, 60-70 (2018).\nZhang, M., et al. A pronounced decline in northern vegetation resistance to flash droughts from\n2001 to 2022. Nat. Commun. 16, 2984 (2025).\nLiu, Z., et al. Increased uptake of deep soil water promotes drought resistance in mixed forests.\nPlant, Cell Environ. 46, 3218-3228 (2023).\nFan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of\nplant rooting depth. Proc. Natl. Acad. Sci. 114, 10572-10577 (2017).\nJing, Y., Wang, S., Chan, P. W. & Yang, Z.-L. Gross primary productivity is more sensitive to\naccelerated flash droughts. Commun. Earth Environ. 6, 34 (2025).\nChen, Z., Qian, Z., Huang, B., Feng, G. & Sun, G. Increased drought impacts on vegetation\nproductivity in drylands under climate change. Geophys. Res. Lett. 52, e2025GL115616 (2025).\n6. Figure 1: Please make sure the units and legend color are consistent for each variable\nacross panels. a and d have different color schemes (yellow-red vs. blue-red) for the\ndrought counts. b and e have different units (90 vs. 0.9) for the drought ratio. c and f have\ndifferent legend text (\"%/pentad\" vs \"Onset speed (%/pentad)\") for the onset speed.\nREPLY: We have unified the color scheme and units of this figure. Please see the revised\nFigure 1 in revision. We copy this figure below as Figure A7.\ncounts, severity and onset speed of flash drought, respectively. (d – f) Distributions of flash\ndrought counts, severity and onset speed in mean annual evaporation (MAE)-precipitation\n(MAP) space, respectively. (g – i) Interannual variations in flash drought counts, severity\nand onset speed anomaly, respectively. The vertical dotted line indicates the year 2002.\nShadow indicated standard deviation. Colored bold and dash lines are the linear fits. The\nresults were tested by Mann-Kendall test: *p < 0.05, **p < 0.01, ***p < 0.001, NS indicate\nno significant.\n7. Lines 124-126: Why is soil moisture considered as one of the climate factors in Figure\nS3?\nREPLY: We understand your concern regarding the comparison of mean soil moisture\nbetween flash drought and slow drought during onset periods. Although we identified the\nflash and slow drought events based on soil moisture data, we only considered the\ndecrease in percentiles of soil moisture during this period. We are interested in the\ndifference in the mean soil moisture conditions between the two types of drought events,\ngiven its crucial effects on water balance, plant water uptake, vegetation growth and\nproductivity, soil moisture climate feedbacks (Qi et al., 2024; Fu et al., 2024; Sun et al.,\n2025).\nfeatures between flash and slow droughts during onset period. The analyses showed\nmarked difference in both drought durations and mean soil moisture between the two types\nof drought events.\nFigure A8. The probability distribution of duration and soil moisture of flash and\nslow droughts. (a) Probability function of flash and slow drought duration. (b) Spatial\npattern in flash and slow drought duration difference. (c) Probability function of soil\nmoisture of flash and slow drought. (d) Spatial pattern in flash and slow drought soil\nmoisture difference.\nReference:\nQi, G., et al. Soil moisture plays an increasingly important role in constraining vegetation\nproductivity in China over the past two decades. Agr. Forest Meteorol. 356, 110193 (2024).\nFu, Z., et al. Global critical soil moisture thresholds of plant water stress. Nat. Commun. 15, 4826\n(2024).\nSun, W., et al. Soil moisture-atmosphere interactions drive terrestrial carbon-water trade-offs.\nCommun. Earth Environ. 6, 169 (2025).\n8. Line 141: Maybe the title could be “Divergence in... responses to flash and slow droughts”\nsince both resilience and resistance were covered in this subsection.\nREPLY: Thank you for your insightful suggestion. The revised sub-title has been changed\n12\nto “Divergence in vegetation productivity response to flash and slow droughts” (Line\n127).\n9. Lines 401-403: Since you are using GPP to represent the vegetation growth, maybe it's\nmore appropriate to use “vegetation productivity” instead of “vegetation growth” in the title\nand related main text because ecologists have traditionally used other proxies to represent\nvegetation growth and vegetation resilience.\nREPLY: Thanks for your valuable comments. We have changed “vegetation growth” to\n“vegetation productivity” throughout the whole manuscript.\n10. Methods lines 471-473: Why does the onset stage end at the first 5-day (3rd pentad)\nwhen the soil moisture is below the 20th percentile for the flash drought, but at the second\n5-day (6th pentad) for the slow drought in Fig S12?\nREPLY: We applied distinct criteria for identifying flash and slow drought events following\nthe method proposed by Yuan et al., 2023.\nFor identifying flash drought, once the soil moisture drops below the 20th percentile,\nthe flash drought onset ends if the average decline rate is less than 5 percentile per\npentad (Figure S15a in supplementary materials). For slow drought, when the soil\nmoisture drops below the 20th percentile, the slow drought onset ends if the soil\nmoisture starts to increase (Figure S15b). We copied Figure S15 as Figure A9 here (see\nbelow).\nWe have clearly stated this information in methods section (Lines 432-443).\nFigure A9. Illustration of the definitions of flash and slow droughts. (a) Flash drought\ndevelopment process. (b) Slow drought development process. The red line in (a) indicates\nsoil moisture percentile changes during flash drought. The blue line in (b) indicates soil\nmoisture percentile changes during slow drought. The filled dots show 5-day mean (pentad)\nsoil moisture percentile in a grid point. The statistics were based on the mean values of\nof 1950 – 2023. The left double arrow line represents the onset period of drought, and the\nright double arrow line represents the recovery of drought. The illustration of specific\nprocesses of flash and slow droughts was shown in Methods.\nReference:\nYuan, X., et al. A global transition to flash droughts under climate change. Science 380, 187-191\n(2023).\n11. Still lines 471-473: But in Fig S12(a), it's at the end, not the beginning, of the recovery\nstage (6th pentad) when the soil moisture begins to increase. Suggest deleting “if the soil\nmoisture level began to increase”.\nREPLY: Our original presentation is not clear and misleading. We have deleted it.\n12. Lines 478-480: This second rule seems to apply to the slow drought as well.\nREPLY: Our former presentation is not clear and misleading. In our revision, we have\nrevised these presentation into, “once the soil moisture drops below the 20th\npercentile, the flash drought onset ends if the average decline rate is less than 5\npercentile per pentad” (Lines 435-436) and “when the soil moisture drops below the\n20th percentile, the slow drought onset ends if the soil moisture starts to increase”\n(Lines 441-442).\n13. Still lines 478-480: Suggest replacing “onset” with “decline”.\nREPLY: Our former presentation is not clear. The onset periods are identified using\ndifferent criteria for flash and slow drought events. For flash drought, soil moisture may still\ncontinue to decline after the onset period, while at a slower decline rate. Please see our\nreply to your comments #10.\nTherefore, we believed “onset” was more appropriate.\n14. Lines 483-485: Should it be “the 40th percentile” or “the percentile of the last pentad\nbefore the onset stage”?\nREPLY: Here, the correct presentation should be “the 40th percentile”. This method\nfollows Yuan et al., 2023.\nReference:\nYuan, X., et al. A global transition to flash droughts under climate change. Science 380, 187-191\n(2023).\n14\n15. Lines 516-517: Did you find drought events during which GPP increased?\nREPLY: We understand your concern. Our analyses revealed that 91.71% of global\nvegetation showed negative anomalies after flash droughts, while 92.39% of global\nvegetation showed negative anomalies after slow droughts. Very few regions showing\npositive GPP anomalies during droughts, and this phenomenon has been widely reported\n(Zhang et al., 2025; O et al., 2024).\nGiven this overwhelming prevalence of GPP decline during drought events, our study\nfocused exclusively on events which vegetation GPP declined. We have clearly stated that\n“We captured drought events where the vegetation time series showed positive\nanomaly before the drought’s occurrence and showed negative anomaly during the\ndrought.” in Lines 497-499. \nReference:\nZhang, M., et al. A pronounced decline in northern vegetation resistance to flash droughts from\n2001 to 2022. Nat. Commun. 16, 2984 (2025).\nO, S. & Park, S. Global ecosystem responses to flash droughts are modulated by background climate\nand vegetation conditions. Commun. Earth Environ. 5, 1-7 (2024).\n16. Lines 517-518: The terms “one growing season recovery” and “multi growing season\nrecovery” were used in Fig S15, please be consistent.\nREPLY: Following your valuable comment, we used the consistent terms of “one growing\nseason recovery” and “multiple growing season recovery” throughout the whole\nmanuscript.\n17. Lines 518-520: What about droughts that persist and frequently happen? Any long-\nterm lags, propagation, legacy effect, or memory of soil moisture resulted?\nREPLY: Thank you for your insightful comments. We thoroughly considered these cases.\nWe have analyzed the probability distribution of drought durations and found flash droughts\ntypically last about 1 pentad shorter than slow droughts, but all of both drought types’ last\n4 – 14 pentads (i.e., 2 weeks to 3 months) (please see the Figure A8 in our reply to your\ncomment #7). Therefore, there is no long lasting drought.\nYes, the repeated droughts could have important and longer memory and propagation\neffects as you mentioned. To address frequent droughts and their legacy effects, we set a\nstrict screening criterion with an interval of 2 years between the end of one drought and\nthe onset of the next. We discarded drought events with intervals between two droughts\nshorter than 2 years. This approach helps eliminate potential legacy effects from frequent\n15\nor overlapping droughts. The threshold of 2-year interval is set up based on the previous\nfindings, showing that vegetation growth could recover at most cases within 2 years (Wu\net al., 2022; Wu et al., 2018).\nThese details are clearly presented in Lines 496-499: “Therefore, we discarded drought\nevents with intervals between two droughts shorter than 2 years. We captured\ndrought events where the vegetation time series showed positive anomaly before\nthe drought’s occurrence and showed negative anomaly during the drought.”.\nReference:\nWu, M., et al. Drought legacy in sub-seasonal vegetation state and sensitivity to climate over the\nNorthern Hemisphere. Geophys. Res. Lett. 49, e2022GL098700 (2022).\nWu, X., et al. Differentiating drought legacy effects on vegetation growth over the temperate\nNorthern Hemisphere. Global Change Biol. 24, 504-516 (2018).\n18. Lines 523-524: Please rephrase it and explain which vegetation index was used and\nwhat the pixel-specific threshold was used for.\nREPLY: We used GPP as the vegetation index to define the growing season and verified\nit with LAI.\nThe growing seasons were identified by multi-year averaged GPP at a pentad scale. The\ngrowing seasons were identified in tropical region and extratropical region, respectively.\nWe used a threshold-based method to retrieve SOS (start of growing season) and EOS\n(end of growing season), which are the points of minimum GPP plus 30% of the annual\namplitude (Li et al., 2023; Zhang et al., 2025). If the minimum GPP exceeds 10 g C m-2\nd-1 (the mean value of tropical GPP), the growing season is considered year-round. We\nalso supplemented with LAI from the GLASS_LAI dataset (Zhang et al., 2020), which\nidentified a similar growing season to GPP. We added a new supporting figure for the\ngrowing season length in supplementary materials (Figure S18), and we copied it here as\nFigure A10.\nWe clearly stated this in Lines 500-506: “Regarding to the identification of growing\nseason, we supposed that the tropics had a year-round growing season, whereas\nthe pixel-specific growing season was determined by a pixel-specific threshold of\nthe minimum value plus 30% of the seasonal amplitude fitting for the multiyear\naveraged GPP for extra-tropical regions (Li et al., 2023; Zhang et al., 2020). The\ngrowing season identification was further verified with leaf area index data (GLASS\ndataset, https://glass.bnu.edu.cn/) (Figure S18 in the supplement martial) (Li et al.,\n2023; Zhang et al., 2020; Ma et al., 2022).”.\nseason based on GPP (gross primary productivity). (b) Spatial pattern in length of growing\nseason based on LAI (leaf area index). The darker the color, the longer the growing season\nlength.\nReference:\nLi, Y., et al. Widespread spring phenology effects on drought recovery of Northern Hemisphere\necosystems. Nat. Clim. Change 13, 182-188 (2023).\nZhang, M., et al. A pronounced decline in northern vegetation resistance to flash droughts from 2001\nto 2022. Nat. Commun. 16, 2984 (2025).\nZhang, Y., Parazoo, N. C., Williams, A. P., Zhou, S. & Gentine, P. Large and projected strengthening\nmoisture limitation on end-of-season photosynthesis. Proc. Natl. Acad. Sci. 117, 9216-9222 (2020).\nMa, H. & Liang, S. Development of the GLASS 250-m leaf area index product (version 6) from MODIS\ndata using the bidirectional LSTM deep learning model. Remote Sens. Environ. 273, 112985 (2022).\n19. Lines 547-549: Did not find the citation for “the previous study”.\nREPLY: In our former manuscript, we wrongly placed the citation on Line 537 (Parazoo et\nal., 2024). We have cited this literature in right place in the revision.\nReference:\nParazoo, N., Osman, M., Pascolini-Campbell, M. & Byrne, B. Antecedent conditions mitigate carbon\nloss during flash drought events. Geophys. Res. Lett. 51, e2024GL108310 (2024).\n20. Lines 556-559: What did you compare the correlation coefficient with? Vegetation\nresilience? What's the purpose of retaining factors with abs(R) less than 0.7? Also, please\nadd the absolute notation in Fig S16.\nREPLY: The presentation in our former version is not clear and misleading. We intend to\nstate that we estimate the correlation matrix among all predicting variables, but not the\ncorrelations between vegetation productivity resilience and predicting variables.\nthe potential collinearity problem among predicting variables. VIF provides a more\ncomprehensive assessment by quantifying how much the variance of an estimated\nregression coefficient increases due to multi-collinearity, making it a more suitable metric\nfor detecting multi-collinearity in multiple regression settings. The factors with a larger VIF\n(VIF > 5) were removed to prevent the occurrence of confusion effects (please see Figure\nS20 in supplementary materials, and we copied it as Figure A11 below).\nWe have added the corresponding description in method section “Prior to performing the\nfinal attribution models, VIF was used to test the collinearity among factors, and the\nfactors with a larger VIF (VIF > 5) were removed to prevent the confusion effects\n(Figure S20a in the supplement marital) (O’brien et al., 2007).” (Lines 550-552).\nFigure A11. Accuracy evaluation of random forest models. (a) Factor collection. (b)\nThe resilience of GPP (gross primary productivity) to flash drought at flash drought hotspots.\n(c) The resilience of GPP to slow drought at flash drought hotspots. (d) The resilience of\nGPP to flash drought at flash drought non-hotspots. (e) The resilience of GPP to slow\ndrought at flash drought non-hotspots. We calculated the VIF (Variance Inflation Factor) of\nall factors, and retained the factors with VIF less than 5 for the final attribution analysis.\nWe considered 15 factors in the attribution analyses, including mean temperature during\ndrought response period, mean radiation during drought response period, mean\nprecipitation during drought response period, mean evaporation during drought response\nperiod, mean soil moisture during drought response period, mean vapor pressure deficit\ndrought response period, mean GPP during drought response period, non-growing\nseason snow water equivalent, preceding GPP (mean GPP in two months preceding\ndrought), CO fertilization effect (β), mean water use efficiency during drought response\n2\nperiod, soil cation exchange capacity, canopy height, maximum root depth, and tree density.\nO’brien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41,\n673-690 (2007).\n21. Lines 559: Why vpd was not included? The correlation coefficients between vpd and\nother factors were also not shown in Fig S16a.\nREPLY: Thank you for your insightful comment. According to the former method of\ncorrelation coefficient matrix, we found that the correlation coefficient between VPD and\ntemperature exceeded 0.7. We further calculated the correlation coefficients between VPD,\ntemperature, and vegetation productivity resilience. We excluded VPD because it has\nlower correlation coefficient with vegetation productivity resilience.\nHowever, we adopted a new and more widely used method, that is VIF criteria, to address\nthe potential collinearity problem (please also see our reply to your comment #20). Based\non the VIF analysis, we found VPD's VIF value is below 5 and it was included into the final\nattribution model. In the Figure S20 (in the supplement marital), we showed the VIF of all\nfactors, including VPD.\nReviewer #1 (Remarks on code availability):\n22. Access to the Matlab code is available through the private link of Code Ocean.\nREADME file is provided with a description for each code script. The figures in the\nmanuscript could be reproduced using the post-processed data the authors uploaded,\nthough the code of data preprocessing (e.g., for producing the aggregated pentad-mean\nsoil moisture) is not available.\nREPLY: Thank you for your suggestion. We have provided the preprocessing codes for\nproducing the aggregated pentad-mean soil moisture. You can find the codes on\nhttps://codeocean.com/signup/nature?token=cb014a417a444476afd84ba6db2880db.\n(Lines 596-598).\nGuo et al quantify drought resistance and resilience of vegetation across the globe. They\nuse a range of vegetation remote sensing datasets like CSIF and FluxSat to characterize\nthese responses of vegetation to flash and slow droughts. They report a range of results\nincluding the prevalence of increasing frequency of flash droughts, less resilience of\nvegetation to flash droughts than slow droughts, and strong relation of CO to drought\n2\nresilience as explained by a random forest model.\nThe methods do appear to be mostly careful and I can appreciate the thought the authors\nput into those, but in general I had trouble following the large breadth of results. I also\nquestion the vegetation response differences claimed between flash and slow drought and\nhotspots and non-hotspots. I think the breadth of results covered here in the analysis are\nactually a weakness in this case. The analysis could be stronger if more honed on fewer\nof these arguments so we can gain confidence in the claims. In general, I had trouble\nfollowing the narrative and am left wondering what the takeaways are. It is unclear if these\nare addressable issues or not but I do think more work is required here. See my comments\nbelow as a guide to improve the manuscript.\nREPLY: Thank you for your insightful comments and constructive suggestions. They are\nextremely helpful for improving the manuscript.\nWe have thoroughly revised the manuscript by incorporating all your comments and\nsuggestions to enhance the clarity and coherence. The additional analyses and major\nchanges were summarized as below.\n1. We strengthened our analyses on the difference in vegetation productivity responses\nto flash and slow drought events at flash drought hotspots and non-hotspots. We\ncomprehensively verified our conclusions.\n2. We added a conceptual diagram for illustrating the core idea of this manuscript, which\nis the spreading of flash drought will decrease the vegetation productivity resilience.\n3. We reorganized the structure of our results and discussions, focusing on the most\nimportant findings regarding the difference in vegetation productivity resilience to flash\nand slow drought events and the underlying driving mechanisms. We streamlined the\nnarrative of our results and the corresponding discussions, which made the manuscript\nmuch clearer and easier to follow.\n4. We re-evaluated the CO fertilization effects spatiotemporally, and distinguished the\n2\nimpacts of CO fertilization effects on the vegetation productivity resilience to flash and\n2\nslow droughts at different regions. These findings highlighted the different sensitivities\nof vegetation productivity resilience to CO fertilization effects.\nto follow and understand.\n1. There is very little difference between flash and slow drought response in figure 3. In\nfact the variability around the medians is very large and I am surprised to see that the\ndifferences were found to be statistically significant. Potentially, this is an artifact of having\nmany data points. Which significance test was used here? This issue castes some doubt\non the differences for flash and slow droughts and I am not sure we can confidently make\nmany of these claims in general in lines 142-165 and throughout figure 3. At least they\ndon’t seem like strong arguments.\nREPLY: Thank you for your insightful and constructive comments. We performed additional\nanalyses to compare the difference in vegetation productivity resilience to flash and slow\ndrought and verify the robustness of our findings.\n(1) The variability around the medians is very large. Which significance test was\nused here?\nThe data visualization in the former version is vague. We first standardized the vegetation\nproductivity proxies (GPP, SIF) prior to our analyses, resulting in a mean of 0 and a\nstandard deviation of 1. This procedure helped highlight differences between flash and\nslow drought responses. Secondly, we performed the normalization test for vegetation\nproductivity resilience to both flash and slow droughts. Our findings showed that these\nvariables meet the normal distribution. Then, we have revised the former boxplot figure to\nbar charts with 95% confidence intervals for clearly showing the difference in vegetation\nproductivity resilience (Figure 3 in the revision, we copied here as Figure B1 below). The\nstatistical significance of our results was tested using one-tailed Student’s t-tests and\nKolmogorov-Smirnov tests at p < 0.05 level. Both methods confirmed that the difference in\nvegetation productivity resilience to flash and slow drought events at both regions are\nstatistically significant (p < 0.05). This significant difference (p < 0.05) remains between\ndifferent regions but under the same drought types. Additionally, bootstrap resampling with\n1,000 iterations also confirmed this statistical significance, reinforcing the robustness of\nour findings. Further, we performed our analysis to 0.5° and\n0.25° spatial resolutions, and the consistent results further verified the robustness of our\nfindings (Figure B2). Our analyses using gridded SIF data also produced the consistent\npatterns (Figure B3).\npattern of mean GPP (gross primary productivity) resilience difference (ΔResilience)\nbetween flash and slow droughts (flash drought minus slow drought). (b) The spatial\npattern of mean GPP resilience to flash drought. (c) The spatial pattern of mean GPP\nresilience to slow drought. (d) Mean GPP resilience to flash and slow droughts within flash\ndrought hotspots and non-hotspots. (e) Mean GPP lag time, resistance and resilience in\ndifferent combinations of flash and slow droughts with flash drought hotspots and non-\nhotspots. Error bars in (d) indicate the 95% confidence intervals of the mean resilience.\nDifferent capital letters in (e) indicate statistical significance (p < 0.05) in resilience to the\nsame drought type between flash drought hotspots and non-hotspots, while different lower-\ncase letters for the same region but different drought types by one-tailed Student’s t-test.\nFigure B2. Difference in GPP resilience to flash and slow droughts at different spatial\nresolutions. (a) Difference in GPP resilience to flash and slow droughts at 1 degree. (b)\nDifference in GPP resilience to flash and slow droughts at 0.5 degree. (c) Difference in\nGPP resilience to flash and slow droughts at 0.25 degree. Error bars indicate the 95%\nconfidence intervals of the mean resilience. Different capital letters indicate statistical\nsignificance (p < 0.05) in resilience between flash drought hotspots and non-hotspots with\nthe same drought type, while different lower-case letters indicate the same region but for\ndifferent drought types by one-tailed Student’s t-test.\npattern of mean SIF (sun-induced fluorescence) resilience difference (ΔResilience)\nbetween flash and slow droughts (flash drought minus slow drought). (b) The spatial\npattern of mean SIF resilience to flash drought. (c) The spatial pattern of mean SIF\nresilience to slow drought. (d) Mean SIF resilience to flash and slow droughts within flash\ndrought hotspots and non-hotspots. (e) Mean SIF lag time, resistance and resilience in\ndifferent combinations of flash and slow droughts with flash drought hotspots and non-\nhotspots. Error bars in (d) indicate the 95% confidence intervals of the mean resilience.\nDifferent capital letters in (e) indicate statistical significance (p < 0.05) in resilience to the\nsame drought type between flash drought hotspots and non-hotspots, while different lower-\ncase letters for the same region but different drought types by one-tailed Student’s t-test.\n(2) Is this an artifact of having many data points?\nWe agree with you that large number of data points can distort the significance test results.\nWe performed two additional analyses to address this issue. First, we examined the\nrobustness of our findings at sub-region within global flash drought hotspots, i.e., humid\nforests in energy-limited areas (with the largest area within flash drought hotspots), and\nfound consistent pattern in vegetation productivity resilience difference (Figure B4).\nConsistent patterns were also observed from other sub-regions, but the results are not\nshowed here to reduce the redundancy. Secondly, we performed the same protocol to\nFluxnet site observations, and obtained the consistent pattern in GPP resilience to flash\nand slow drought events (Figure B5). These additional analyses verified that our findings\nare robustness and might not be only the artifact of large data sets.\nin energy-limited region. (a) The spatial pattern of mean GPP (gross primary productivity)\nresilience to flash drought. (b) The spatial pattern of mean GPP resilience to slow drought.\n(c) GPP resilience in different combinations of flash and slow droughts with flash drought\nhotspots and non-hotspots. Error bars indicate the 95% confidence intervals of the mean\nresilience. Different capital letters indicate statistical significance (p < 0.05) in resilience\nbetween flash drought hotspots and non-hotspots with the same drought, while different\nlower-case letters indicate the same region but different drought by one-tailed Student’s t-\ntest.\nFigure B5. Difference in GPP resilience to flash and slow droughts based on Fluxnet\nsites. Error bars indicate the 95% confidence intervals. Different capital letters indicate\nstatistical significance (p < 0.05) between flash drought hotspots and non-hotspots with the\nsame drought type, while different lower-case letters indicate the same region but different\ndrought types by one-tailed Student’s t-test.\nAccordingly, we have revised the results section: “The global mean response lag time\nto flash drought was significantly shorter than that to slow drought (Δ = -1 ± 0.29\npentads at flash drought hotspots; Δ = -3 ± 0.27 pentads at non-hotspots; both p <\n0.05) (Figure 3e in our revision). The mean GPP resistance to flash drought was\nsignificantly lower than that to slow drought at flash drought non-hotspots (Δ = -\n0.09 ± 0.03, p < 0.001), whereas there was no significant difference at flash drought\nhotspots (Figure 3e in our revision).” (Lines 131-136) and “The mean GPP resilience\nto flash drought (0.03 ± 0.56) was significantly higher than that to slow drought (-\n0.01 ± 0.56) (p < 0.01) at flash drought hotspots (Figure 3d, e in our revision).\nHowever, an opposite pattern was observed in GPP resilience (0.10 ± 0.68 and 0.15\n± 0.71 for flash and slow droughts, respectively, p < 0.01) at non-hotspots (Figure\n3d, e in our revision). We further found that the GPP resilience to both flash and slow\ndroughts at flash drought hotspots was significantly lower than that at non-hotspots\n(Δ = -0.07 ± 0.02 and Δ = -0.16 ± 0.02, respectively, both p < 0.001) (Figure 3d in our\nrevision).” (Lines 141-148).\n2. There are many arguments being made throughout the study about flash drought\nchanges in time, differences between slow and fast drought, differences between hotspots\nand non-hotspots, and then the many factors explaining the differences in patterns. I’ll\n24\nadmit I really struggled to follow the story throughout following these different threads.\nPotentially, one aspect that can help is to merge the results and discussions sections more\nbecause so much material is covered in both. It requires a lot of bouncing back and forth\nbetween the figures, results text, and discussion text. Another point that can help is to trim\nout the arguments that aren’t contributing much and focus more on the messages that do.\nREPLY: Thank you very much for your constructive comments and suggestions, which are\ncrucial for improving the manuscript.\nWe reorganized the structure of the manuscript and focused mainly on the divergence in\nvegetation productivity resilience to flash and slow drought and the underlying drivers. We\nfirst identified the spatiotemporal pattern in flash and slow drought events and highlighted\nthe increasing spread of flash drought globally. Then, we disentangled the significant\ndifference in vegetation productivity resilience to flash and slow drought events at two\ntypical regions (flash drought hotspots and non-hotspots). Finally, we understand the\ndifference in the underlying drivers for the divergence in vegetation productivity resilience\nto two drought types. \nWe thoroughly revised our discussions and now it is in a much clearer logical way and easy to\nfollow.\nAdditionally, we added a conceptual diagram (Figure 6 in our revision, and we copied it\nhere as Figure B6) to illustrating the storyline of our study. We incorporating this figure into\nthe discussions. This figure can help readers to better understand the core idea of our\nstudy.\nassociated threatened vegetation productivity resilience. There shows an\nintensification and spread of flash droughts, and the climate stress intensifies during this\ntransition. Flash drought reduces the sensitivity of vegetation productivity resilience to CO\n2\nfertilization effects. The resilience of vegetation productivity to flash drought is weaker in\nflash drought hotspots than non-hotspots. Flash drought is spreading rapidly from its\nhotspots to non-hotspots. Larger size of block areas indicates greater effects.\n3. I am suspicious about why there are so many flash droughts in humid locations and so\nfew in dry locations. I in general agree with how the droughts and flash droughts are\nidentified with the different criteria starting in line 469. However, I am unsure about the\n5%/pentad definition for flash drought. This should be a relative change and not a\nmagnitude change. Otherwise, dry locations will rarely have high enough soil moisture to\nlose moisture at this rate. It should be based on a relative change to normalize across\ndifferent conditions.\nREPLY: We are sorry for that the former presentation is not clear and misleading. Yes, you\nare right and the identification of flash and slow drought events are based on the relative\nchanges in soil moisture percentiles.\nSpecifically, we determined flash drought events following the method proposed by Yuan\net al., 2023. In each grid pixel, the soil moisture was aggregated into a 5-day average\n(pentad), which was then converted into percentiles after deseasonalization and\ndetrending. We determined the flash and slow drought events based on the changes in\n26\nthese soil moisture percentiles. We illustrated the data processing using observations from\na single pixel (latitude: 41°N, longitude: 118°W), which was showed in Figure B7.\nFigure B7. Soil moisture data of a single pixel and processing. (a) Soil moisture time\nseries of one grid (latitude: 41°N, longitude: 118°W) (1950 – 2023). (b) Soil moisture\npercentiles (1950 – 2023).\nThere shows higher frequency of flash droughts in humid locations compared to dry ones,\nand this phenomenon was widely confirmed (Yuan et al., 2023; Christian et al., 2021).\nHumid regions are characterized by abundant water availability. When there is less rainfall\nduring the rainy season, strong solar radiation and high-temperature heatwaves can\naccelerate water loss (Yuan et al., 2023; Christian et al., 2021; Zeng et al., 2023).\nAdditionally, vegetation in humid areas is typically lush (Zhang et al., 2022; Zhao et al.,\n2025). Plants can draw soil moisture from different soil layers, which increases\nevapotranspiration and makes these regions more susceptible to flash drought conditions\n(Zhang et al., 2022; Liu et al., 2023; Chen et al., 2021).\nWe have expanded the discussion by adding, “Humid regions are characterized by\nabundant water supply. When there is less rainfall during the rainy season, strong\nsolar radiation and high-temperature heatwaves can accelerate water loss (Yuan et\nal., 2023; Christian et al., 2021; Zeng et al., 2023). Additionally, vegetation in humid\nregions is typically lush (Zhang et al., 2022; Zhao et al., 2025). In facing of water\nshortage, vegetation can draw a large amount of soil moisture from soil layers,\nwhich facilitates increased evapotranspiration and makes these regions more\nsusceptible to flash drought conditions (Zhang et al., 2022; Liu et al., 2023; Chen et\nal., 2021).” (Lines 187-192).\nReference:\nYuan, X., et al. A global transition to flash droughts under climate change. Science 380, 187-191\n(2023).\nChristian, J. I., et al. Global distribution, trends, and drivers of flash drought occurrence. Nat.\nCommun. 12, 6330 (2021).\nglobal warming. npj Clim. Atmos. Sci. 6, 134 (2023).\nZhang, M., Yuan, X., Otkin, J. A. & Ji, P. Climate warming outweighs vegetation greening in\nintensifying flash droughts over China. Environ. Res. Lett. 17, 054041 (2022).\nZhao, Z., Zhou, Y., Ju, W., Xiao, J. & Li, X. Widespread and divergent post-drought loss of gross\nprimary production. Geophys. Res. Lett. 52, e2025GL115825 (2025).\nLiu, Z., et al. Increased uptake of deep soil water promotes drought resistance in mixed forests.\nPlant, Cell Environ. 46, 3218-3228 (2023).\nChen, L., Ford, T. W. & Yadav, P. The role of vegetation in flash drought occurrence: a sensitivity study\nusing community earth system model, version 2. J. Hydrometeorol. 22, 845-857 (2021).\n4. I am not sure what we are learning from the attribution analysis in Figures 4 and 5. I am\nadmittedly having trouble articulating how I am not following everything but will give a few\nexamples here. First of all, it is unclear whether relationships are being evaluated spatially\nor temporally and therefore I am not sure how to interpret whether a variable “controls”\nvegetation drought resilience (as describing spatial gradients, or describing how they\nrespond in time). Second, the perceived processes governing how the variables are related\nare a bit unclear. The CO concentration itself is not considered a fast-acting variable that\n2\ncan directly change a plant response to drought. Effectively, all plants should be\nexperiencing the same mean levels of CO because they are well mixed, so I am guessing\n2\nthis analysis is being conducted temporally? Otherwise, water use efficiency or a stomatal\ntrait is potentially more suited in place of CO . This could be an issue of timescales where\n2\nrelated to trends in the drought metrics. As a counterargument to show my point: if the\nanalysis is conducted over 1-5 years, CO may no longer be a large explanatory variable.\nquite clear what we have learned from this analysis. The differences in hotspots and slow\nand fast drought are so small as well that I don’t think there is much information presented\nother than we can’t distinguish much from what the attribution analysis is showing.\nREPLY: Thank you very much for your constructive comments and valuable suggestions.\nIt is indeed one of the most important points for this study.\nWe performed additional analyses regarding the CO fertilization effects on vegetation\n2\nproductivity resilience following your suggestions.\nFirst, we evaluated the CO fertilization effects on GPP at drought event level\n2\n(spatiotemporal dimension) using the global detrended and deseasonalized GPP and CO\n2\nconcentrations following the method which proposed by Wang et al., 2023 (i.e.,\n𝜕𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦⁄𝜕𝐶𝑂 , named β hereafter). This means the β considered here reflects the\n2\n“fast” (ranging decadal to seasonal scales) impacts of variations in CO on variations in\n2\nlonger time scale (decades to centuries) (Chen et al., 2022; Soumaya et al., 2025). By\ncontrast, the variations in CO concentrations could impact GPP variations through its\n2\neffects on stomatal conductance (Liang et al., 2023; Keeling et al., 2017), carboxylation\n28\nefficiency (Lu et al., 2020), water use efficiency (Wu et al., 2018; Soh et al., 2019), plant\nenzyme activity (Sophie et al., 2025; Lambers et al., 2019), and plant filution effect of\nmineral elements (i.e., caused by the accumulation of non-structural carbohydrates) (Li et\nal., 2021), particularly under extreme climate conditions (e.g., under strong drought stress)\n(Guo et al., 2025). Previous research has reported that CO fertilization could dramatically\n2\nincrease plant biomass (Terrer et al., 2019). Therefore, the β (spatiotemporal dimension)\nconsidered in this study matches the drought events.\nWe then investigated the impacts of diverse factors on the drought event-based vegetation\nproductivity resilience. These factors are regrouped into six groups (Table B1), including β\n(spatiotemporal), anomalies in climate conditions (spatiotemporal, event-based),\nvegetation conditions preceding and during drought (spatiotemporal), vegetation property\n(maximum rooting depth, spatial), stand features (tree density, canopy height, spatial) and\nsoil property (soil cation exchange capacity, spatial). The absolute importance of each\npredicting variable on the vegetation productivity resilience was estimated by the\npermutation importance method in random forest models (in detail please see Lines 525-\n528 in our revision).\nTable B1. The spatio-temporal dimensions of each factor.\nCategories Factors Dimension\nTemperature during drought response\nClimate Spatio-temporal\nperiod\nRadiation during drought response\nClimate Spatio-temporal\nperiod\nPrecipitation during drought response\nClimate Spatio-temporal\nperiod\nEvaporation during drought response\nClimate Spatio-temporal\nperiod\nSoil moisture during drought response\nClimate Spatio-temporal\nperiod\nClimate VPD during drought response period Spatio-temporal\nNon-growing season snow water\nClimate Spatio-temporal\nequivalent\nVegetation GPP anomalies during drought\nSpatio-temporal\nConditions response period\nVegetation GPP anomalies in two months\nSpatio-temporal\nConditions preceding drought\nVegetation\nWUE during drought response period Spatio-temporal\nConditions\nCarbon dioxide Carbon dioxide fertilization effect Spatio-temporal\nSoil Property Soil cation exchange capacity Spatial\nStand Property Tree density Spatial\nStand Property Canopy height Spatial\nWe included the WUE (event-based) into our final attribution models, following your\nsuggestions. We found that there is a generally positive relationship between WUE and\nvegetation productivity resilience (Figure S10 in the supplementary materials, and we\ncopied it as Figure B8 below). However, its importance is lower than the β, and this implying\nthat short-term CO variations could impact GPP variations through diverse pathways other\n2\nthan WUE (see above).\nIn the revision, we accordingly revised the methods, “We considered 15 factors in\nthe attribution analyses, which can be grouped into 6 categories, including climate\nfactors (spatio-temporal), CO fertilization effect (spatio-temporal), vegetation\n2\nresponse features (spatio-temporal), soil property (spatial), plant trait (spatial) and\nstand features (spatial).” in Lines 525-528.\nFigure B8. Heat map of the correlation coefficients between GPP resilience and\ndifferent factors. The factors included mean temperature during drought response period,\nmean radiation during drought response period, mean precipitation during drought\nresponse period, mean evaporation during drought response period, mean soil moisture\nduring drought response period, mean vapor pressure deficit during drought response\nperiod, non-growing season snow water equivalent, β (carbon dioxide fertilization effect),\nmean water use efficiency during drought response period, preceding GPP (mean GPP in\ntwo months preceding drought), mean GPP during drought response period, soil cation\nexchange capacity, canopy height, maximum rooting depth and tree density. The darker\ncolors indicate higher correlation coefficients.\nRegarding to your concern of the timescale impacts on CO effects, we performed\n2\nadditional analyses in a short period of 2001 – 2005, using the former protocol (i.e., using\nthe raw CO concentrations but not the β). The results obtain the consistent pattern in the\n2\nincluded in our revision, given we re-analyzed the attribution of different factors to\nvegetation productivity resilience using a new protocol, by including the β.\n2005. Importance of different factors was estimated by random forest models dependent\non GPP resilience for flash and slow droughts at flash drought hotspots and non-hotspots,\nrespectively. The factors included mean temperature during drought response period,\nmean radiation during drought response period, mean precipitation during drought\nresponse period, mean evaporation during drought response period, mean soil moisture\ndrought response period, mean vapor pressure deficit during drought response period,\nnon-growing season snow water equivalent, carbon dioxide concentrations, mean\nwater use efficiency during drought response period, preceding GPP (mean GPP in two\nmonths preceding drought), mean GPP during drought response period, soil cation\nexchange capacity, canopy height, maximum rooting depth and tree density. The darker\ncolors indicate higher importance.\nRegarding the importance of the remainder of the variables, they are close in their\nexplanatory values from the Figure 4a (in our revision) and Figure 5a (in our revision). We\nadmitted that the difference in importance of those variables between flash and slow\ndrought is not clear. Instead, we place greater emphasis on the partial dependence of\nvegetation productivity resilience to these factors, which exhibited marked difference in the\nsensitivity of productivity resilience to variations in some of those factors. For example, we\ndiscover that GPP resilience is less sensitive to increased CO fertilization during flash\n2\ndrought compared to slow drought (Figure 4b in our revision, we copied it here as Figure\nB10). GPP resilience is more sensitive to decreases in GPP anomalies during and prior to\nflash drought than slow drought (Figure 4c, f in our revision). Furthermore, along with\nincrease in radiation, temperature, and evaporation, the resilience of GPP to flash drought\ndeclines more rapidly than to slow drought (Figure 4e, g–k in our revision). These results\nclearly disentangled the impacts of those factors on the vegetation productivity resilience\nto flash and slow droughts. We have rewritten the results section based on the new\nanalyses (please see Lines 163-169 in our revision).\nresilience to flash and slow droughts at flash drought hotspots. (a) The importance\nof diverse factors for the GPP (gross primary productivity) resilience to flash and slow\ndroughts at flash drought hotspots derived from the random forest model, including β\n(carbon dioxide fertilization effect), mean GPP during drought response period, maximum\nrooting depth, mean soil moisture during drought response period, preceding GPP (mean\nGPP in two months preceding drought), mean temperature during drought response period,\nmean vapor pressure deficit during drought response period, mean radiation during\ndrought response period, mean precipitation during drought response period, mean\nevaporation during drought response period, soil cation exchange capacity, canopy height,\nnon-growing season snow water equivalent, mean water use efficiency during drought\nresponse period, and tree density. (b) β. (c) Mean GPP during drought response period.\n(d) Maximum rooting depth. (e) Mean soil moisture during drought response period. (f)\nPreceding GPP. (g) Mean temperature during drought response period. (h) Mean vapor\npressure deficit during drought response period. (i) Mean radiation during drought\nresponse period. (j) Mean precipitation during drought response period. (k) Mean\nevaporation during drought response period. The red and blue points in (a) indicate factor\nimportance for flash and slow droughts at flash drought hotspots, respectively. The red and\nblue curves in (b) – (k) indicate the response curve of GPP resilience to each major factor\nunder flash and slow droughts at flash drought hotspots, respectively. The shadows\nindicate the 95% confidence intervals.\nReference:\nWang, S., et al. Recent global decline of CO fertilization effects on vegetation photosynthesis.\nChen, C., et al. CO fertilization of terrestrial photosynthesis inferred from site to global scales. P.\n2\nSoumaya, B., et al. Precipitation alters the CO effect on water-use efficiency of temperate forests.\nLiang, X., et al. Stomatal responses of terrestrial plants to global change. Nat. Commun. 14, 2188\n(2023).\nKeeling, R., et al. Atmospheric evidence for a global secular increase in carbon isotopic\ndiscrimination of land photosynthesis. P. Natl. Acad. Sci. 114, 10361-10366 (2017).\nLu, X., et al. Maximum Carboxylation Rate Estimation With Chlorophyll Content as a Proxy of\nRubisco Content. J. Geophys. Res.: Biogeo. 125, e2020JG005748 (2020).\nWu, X., et al. Atmospheric Water Demand Dominates Daily Variations in Water Use Efficiency in\nAlpine Meadows, Northeastern Tibetan Plateau. J. Geophys. Res.: Biogeo. 124, 2174-2185 (2019).\nSoh, W. K., et al. Rising CO drives divergence in water use efficiency of evergreen and deciduous\n2\nplants. Sci. Adv. 5, eaax7906 (2019).\nSophie, A. Z., et al. Centennial-scale atmospheric CO rise increased photosynthetic efficiency in a\n2\ntropical tree species. New Phytol. 246, 131-143 (2025).\nLambers, W. K., et al. Photosynthesis, Respiration, and Long-Distance Transport: Photosynthesis.\nPlant Physiol. Eco.. 1, 11-114 (2019).\nLi, J., et al. Does the short-term fluctuation of mineral element concentrations in the closed\nhydroponic experimental facilities affect the mineral concentrations in cucumber plants exposed\nto elevated CO ? Plant Soil 465, 125-141 (2021).\nGlobal Change Biol. 31, e70129 (2025).\nTerrer, C., et al. Nitrogen and phosphorus constrain the CO fertilization of global plant biomass.\nMore specific comments\n1. L23-25: at this point, quantities in the parentheses are not well defined. Suggest\nclarifying or removing.\nREPLY: Thank you for your suggestion. We have removed the quantities in the revision.\n2. L92: it would be worth giving context early how flash drought and slow drought differ.\nREPLY: Thank you for your valuable suggestion. We have revised this sentence and added\na clear statement for the difference between the two types of droughts. We added: “The\nflash and slow droughts were determined by the average decline in onset speed,\nwith flash drought no less than 5 percentile per pentad and slow drought less than\n5 percentile per pentad (see Methods).” (Lines 88-90).\n3. L126: again, a definition of what a flash drought hotspot is would be very helpful. It also\nfeels a bit strange to have this seemingly main result all be in the SI.\nREPLY: Thank you for your constructive suggestion. In the revision, we applied the MFDI\n(multivariate flash drought indicator), which integrates flash drought counts, severity, and\n33\nonset speed, to identify global flash drought hotspots following the methods proposed by\nMukherjee et al., 2022.\nWe have clearly illustrated the calculation of MFDI and the definition of flash drought\nhotspots based on MFDI. Flash drought hotspots are those regions where the MFDI mean\nvalues exceed 100 (Figure 2 in our revision, we copied it here as Figure B11). This\ndefinition is clearly stated in results section “We then identified global hotspots of flash\ndrought based on the magnitude of MFDI (see Methods). A higher magnitude of MFDI\nover 100 indicates that flash drought events are relatively more frequent, severe,\nand flashy (Figure 2a in our revision), and thus regions with MFDI higher than 100\ncan be considered as the flash drought hotspots (Figure 2b in our revision).” (Lines\n114-117) and the detailed calculation procedures were clearly presented in methods (Lines\n471-489).\nFigure B11. The spatial pattern of flash drought hotspots and non-hotspots. (a) 3D\nscatter plot showing the association between the flash drought counts, severity, onset\nspeed and multivariate flash drought indicator (MFDI) across study region. (b) MFDI in 29\nregions, arranged in descending order (from left to right) of the mean MFDI magnitude. (c)\nSpatial pattern in flash drought hotspots and non-hotspots. (d) Difference in MFDI between\nflash drought hotspots and non-hotspots. (e) Difference in the mean trends of MFDI during\n1950 – 2023 between flash drought hotspots and non-hotspots. The trends were estimated\nby the Kendall τ method. WLHGra indicates water-limited humid grass. WLHCro indicates\nwater-limited humid crop. WLSHFor indicates water-limited sub-humid forest. ELHFor\nindicates energy-limited humid forest. ELHGra indicates energy-limited humid grass.\nDifferent capital letters in (d) and (e) indicate statistical significance (p < 0.05) in MFDI\nvalue and mean trends between flash drought hotspots and non-hotspots by one-tailed\nStudent’s t-test.\nMukherjee, S. & Mishra, A. K. A multivariate flash drought indicator for identifying global hotspots\nand associated climate controls. Geophys. Res. Lett. 49, e2021GL096804 (2022).\n4. L153: are we just looking at differences of drought response in difference climatic regions?\nREPLY: Thank you for your insightful comments.\nThe main storyline of this study is to understand the spread of flash drought (i.e., the\nintensification of flash drought is much stronger in flash drought non-hotspots than hotspots)\nand their crucial impacts on vegetation productivity resilience. So, we focused on the\ndivergence in vegetation productivity resilience to flash and slow drought events in flash\ndrought hotspots and non-hotspots.\nThere may be too many results and more storylines if we compared the difference in\nvegetation productivity resilience among different biomes or ecoregions. It will be very hard\nto follow. As you and the third reviewer pointed that we should streamline the results and\nfocused on the most important findings and made the manuscript easier to follow.\nTherefore, we highlighted the divergence in vegetation productivity resilience to flash and\nslow drought at flash drought hotspots and non-hotspots globally. Notably, we investigated\nthe difference in vegetation traits (e.g., maximum rooting depth) and the climate sensitivity\nof vegetation productivity resilience between the two regions. We accordingly discussed\nour findings in context of multifaceted features in the two regions, not only the climate\nconditions, but also the vegetation features and vegetation growth climate sensitivity.\n5. L157: a definition of resilience and resistance would be helpful.\nREPLY: We added the definitions of resilience and resistance in the revision. Resistance\nis quantified as the difference between the maximum negative anomaly before the recovery\nprocess (lag point) (𝑌) and the multi-year growing season mean (𝑌 ) (Zhang et al., 2025). \n𝑙 𝑚\nResilience is calculated as the mean of vegetation anomalies after the drought (post-\ndrought) (𝑌 ) minus the multi-year growing season mean (𝑌 ) (Lloret et al., 2011). We\n𝑝 𝑚\nhave added definitions of resistance and resilience as follows: “… resistance of GPP (i.e.,\nthe maximum negative anomaly of GPP from multi-year mean)” (Line 129-130) and\n“The GPP resilience is quantified by the difference between mean GPP anomalies\nwithin two years after the drought and the multi-year mean growing season GPP.”\n(Lines 138-139).\nReference:\nZhang, M., et al. A pronounced decline in northern vegetation resistance to flash droughts from\n2001 to 2022. Nat. Commun. 16, 2984 (2025).\nLloret, F., Keeling, E. G. & Sala, A. Components of tree resilience: effects of successive low-growth\nepisodes in old ponderosa pine forests. Oikos 120, 1909-1920 (2011).\n6. L168-169: in time? In space?\nREPLY: Our former presentation is not clear. We considered 15 factors in the final\nattribution analyses, which are regrouped into 6 categories. In our revision, we clearly\nindicated the spatial and/or temporal dimensions for these factors. Please also see our\nreply to your major comment #4 and Table B1.\nIn the revision, we have added the following sentences, “We considered 15 factors in\nthe attribution analyses, which can be grouped into 6 categories, including climate\nfactors (spatio-temporal), CO fertilization effect (spatio-temporal), vegetation\n2\nresponse features (spatio-temporal), soil property (spatial), plant trait (spatial) and\nstand features (spatial).” (Lines 525-528).\n7. L172: I don’t understand this statement. Is this increasing CO in time or CO time\n2 2\nvariability? It is unclear if these are relationships in time, space, or both.\nREPLY: Thank you for your constructive comments, which are helpful for improving the\nmanuscript.\nIn the former version, we used the temporal variations in CO concentration in the\n2\nattribution analyses.\nHowever, we applied a new indicator for the CO effect on the vegetation productivity\n2\nresilience, that is the CO fertilization effect, β, following your valuable suggestion in the\n2\nrevision. The β quantified spatio-temporal CO fertilization effect. We clearly illustrated the\n2\nquantification of β in the revision (in detail see Lines 540-544 in our revision). Please also\nsee our reply to your major comment #4.\nIn the revision, we clearly stated, “…, CO fertilization effect (spatio-temporal) …” in\n8. L224-226: the lack of definitions, such as how hotspots are defined, in the main text\nmakes it challenging to follow this discussion.\nREPLY: In the revision, we clearly provided the definitions of flash drought hotspots and\nnon-hotspots and other terms.\nWe introduced the MFDI (multivariate flash drought index) for the identification of flash\ndrought hotspots. A region is designated as a flash drought hotspot when its MFDI mean\nvalue surpasses 100 (see details in our responses to your specific comment #3).\nthat flash drought events are relatively more frequent, severe, and flashy (Figure 2a\nin our revision), and thus regions with MFDI higher than 100 can be considered as\nthe flash drought hotspots (Figure 2b in our revision).” (Lines 115-117). We added\ndetailed description in the methods section (Lines 471-489).\n9. L248-250: maybe the more direct variable acting through CO is water use efficiency or\n2\nstomatal strategies (including c3 versus c4)? I am generally struggling to follow how CO\n2\nis such a strong factor explaining drought strategies.\nREPLY: Thank you for your insightful comments.\nFollowing your valuable suggestion, we re-evaluated the CO fertilization effect following\n2\nthe methods proposed by Wang et al., 2020. The CO fertilization effect defined in our\n2\nrevision, β hereafter, reflects the short-term effects on the vegetation drought responses.\nPrevious studies have demonstrated that variations in CO at short-term scales (e.g.,\n2\nmonths, matching the drought event scale) have crucial impacts on stomatal conductance\n(Liang et al., 2023; Keeling et al., 2017), carboxylation efficiency (Lu et al., 2020) and\nwater use efficiency (Wu et al., 2018; Soh et al., 2019), plant enzyme activity (Sophie et\nal., 2025; Lambers et al., 2019), and plant filution effect of mineral elements (i.e., caused\nby the accumulation of non-structural carbohydrates) (Li et al., 2021), hence exerted\nsignificant effects on GPP. Importantly, previous studies revealed that higher CO\n2\nconcentrations could alleviate the drought impacts on vegetation productivity, through the\ninteracting effects of CO and drought stress (Chen et al., 2024; Chen et al., 2021).\n2\nanalyses and found that it is the most important individual factor for explaining vegetation\nproductivity resilience (short-term event-based). We further verified our findings using\ndifferent vegetation productivity proxy, i.e., SIF, and found consistent pattern. Please also\nsee our reply to your major comment #4.\nRegarding to your suggestion of considering C3 and C4 vegetation, we considered this\nvaluable point and used the daily GPP dataset (FluxSat), which has already considered\nthe differences in CO absorption efficiency between C3 and C4 vegetation. Specifically,\n2\nvegetation was categorized into two classes using satellite measurements: highly\nproductive vegetation, such as C4 maize with high relative annual maximum GPP values\nfound in crop regions, and all other vegetation types, including C3 plants (Joiner et al.,\n2020). FluxSat dataset have been widely used for vegetation response to drought in\nprevious studies (Kannenberg et al., 2024; Liu et al., 2025).\nWe added the following sentences into the revision, “FluxSat dataset has considered\nthe differences in CO absorption efficiency between C3 and C4 vegetation (Joiner\n2\net al., 2020). Specifically, vegetation was separated into two classes using satellite\nmeasurements: (1) highly productive (e.g., in crop regions such as C4 maize with\n37\nhigh relative annual maximum GPP values) and (2) all others (e.g., C3 vegetation)\n(Joiner et al., 2020).” in Lines 359-363.\nReference:\nWang, S., et al. Recent global decline of CO fertilization effects on vegetation photosynthesis.\nLiang, X., et al. Stomatal responses of terrestrial plants to global change. Nat. Commun. 14, 2188\n(2023).\nKeeling, R., et al. Atmospheric evidence for a global secular increase in carbon isotopic\ndiscrimination of land photosynthesis. P. Natl. Acad. Sci. 114, 10361-10366 (2017).\nLu, X., et al. Maximum Carboxylation Rate Estimation With Chlorophyll Content as a Proxy of\nRubisco Content. J. Geophys. Res.: Biogeo. 125, e2020JG005748 (2020).\nWu, X., et al. Atmospheric Water Demand Dominates Daily Variations in Water Use Efficiency in\nAlpine Meadows, Northeastern Tibetan Plateau. J. Geophys. Res.- Biogeo. 124, 2174-2185 (2019).\nSoh, W. K., et al. Rising CO drives divergence in water use efficiency of evergreen and deciduous\n2\nplants. Sci. Adv. 5, eaax7906 (2019).\nSophie, A. Z., et al. Centennial-scale atmospheric CO rise increased photosynthetic efficiency in a\n2\ntropical tree species. New Phytol. 246, 131-143 (2025).\nLambers, W. K., et al. Photosynthesis, Respiration, and Long-Distance Transport: Photosynthesis.\nPlant Physiol. Eco.. 1, 11-114 (2019).\nLi, J., et al. Does the short-term fluctuation of mineral element concentrations in the closed\nhydroponic experimental facilities affect the mineral concentrations in cucumber plants exposed\nto elevated CO ? Plant Soil 465, 125-141 (2021).\n2\ncontributed to gross primary productivity increase more than climate and forest cover changes in\nsubtropical forests of China. Biogeosciences 21, 2253-2272 (2024).\nChen, L., Ford, T. W. & Yadav, P. The role of vegetation in flash drought occurrence: a sensitivity\nstudy using community earth system model, version 2. J. Hydrometeorol. 22, 845-857 (2021).\nJoiner, J. & Yoshida, Y. Satellite-based reflectances capture large fraction of variability in global gross\nprimary production (GPP) at weekly time scales. Agric. For. Meteorol. 291, 108092 (2020).\nKannenberg, S. A., Anderegg, W. R., Barnes, M. L., Dannenberg, M. P. & Knapp, A. K. Dominant\nrole of soil moisture in mediating carbon and water fluxes in dryland ecosystems. Nat. Geosci. 17,\n38-43 (2024).\nLiu, Z., et al. Enhanced vegetation productivity driven primarily by rate not duration of carbon uptake.\nNat. Clim. Change 15, 560-568 (2025).\n10. L392-394: I am not seeing where this result is represented in Figure 4.\nREPLY: In our revision, we calculated the total contribution of all climate factors to\nvegetation productivity resilience to flash drought at non-hotspots. We found that climate\nfactor category has the highest contribution (32.29 ± 2.05%) than other categories (Figure\nB12), and it implies they play a more significant role in shaping GPP resilience to flash\ndroughts at non-hotspots. As climate stress intensifies (i.e., the warmer conditions and\n38\ngreater degree of water loss), the vegetation productivity resilience declines at non-\nhotspots (Figure S12 in the revised supplementary materials, we copied it here as Figure\nB13).\nFigure B12. The contribution of factor categories for GPP resilience to flash drought\nat non-hotspots. The green bar chart indicates mean contribution values for each factor\ncategories, including carbon dioxide (CO ), climate, vegetation, traits, stand and soil\n2\ncategories.\nFigure B13. The importance of factors and partial dependency plots for GPP\nresilience to flash drought at flash drought hotspots and non-hotspots. (a) The\nimportance of diverse factors for the GPP (gross primary productivity) resilience to flash\ndrought at flash drought hotspots and non-hotspots derived from the random forest model,\nincluding β (carbon dioxide fertilization effect), mean GPP during drought response period,\nmaximum rooting depth, mean temperature during drought response period, preceding\nGPP (mean GPP in two months preceding drought), mean radiation during drought\nresponse period, mean vapor pressure deficit during drought response period, mean\nprecipitation during drought response period, mean evaporation during drought response\nperiod, mean soil moisture during drought response period, non-growing season snow\nwater equivalent, soil cation exchange capacity, canopy height, tree density, and mean\n39\nwater use efficiency during drought response period. (b) β. (c) Mean GPP during drought\nresponse period. (d) Maximum rooting depth. (e) Mean temperature during drought\nresponse period. (f) Preceding GPP. (g) Mean radiation during drought response period.\n(h) Mean vapor pressure deficit during drought response period. (i) Mean precipitation\nduring drought response period. (j) Mean evaporation during drought response period. (k)\nMean soil moisture during drought response period. The orange and green points indicate\nfactor importance in (a) for flash drought at flash drought hotspots and non-hotspots,\nrespectively. The orange and green curves in (b) – (k) indicate the responses of GPP\nresilience to each major factor at flash drought hotspots and non-hotspots, respectively.\nThe shadows indicate the 95% confidence intervals.\n11. L477: is this 5% by magnitude of soil moisture or a 5% relative change? This makes a\nbig difference and might explain why so few dry locations see flash droughts based on this\ndefinition. Soil moisture is already low and near a lower bound and thus is unable to drop\n5% by magnitude.\nREPLY: We are sorry that the former presentation is not clear and misleading. Here is the\nrelative change of the soil moisture percentiles. We clearly illustrated the determination of\nflash and slow drought events in the revision (Lines 432-443). Please see details in our\nresponse to your major comment #3.\n12. L549-553: are these spatial and temporal changes of these variables? Or just temporal?\nREPLY: Our former information for these factors is not clear. We considered 15 factors in\nthe final attribution analyses, which are regrouped into 6 categories. In our revision, we\nclearly indicated the spatial and/or temporal dimensions for these factors. Please also see\nour reply to your major comment #4 and Table B1.\nIn the revision, we have added the following sentences, “We considered 15 factors in\nthe attribution analyses, which can be grouped into 6 categories, including climate\nfactors (spatio-temporal), CO fertilization effect (spatio-temporal), vegetation\n2\nresponse features (spatio-temporal), soil property (spatial), plant trait (spatial) and\nstand features (spatial).” (Lines 525-528).\n13. Figure 3: the differences reported here are very small. Which significance tests were\nused?\nREPLY: Thank you for your insightful comment.\nThe difference between vegetation productivity resilience to flash and slow drought is not\nclear in the former boxplot, due to the large range scale. In the revision, we performed\nthe normalization test for vegetation productivity resilience to both flash and slow droughts.\nOur findings showed that these variables meet the normal distribution. Then, we have\nrevised the former boxplot figure to bar charts with 95% confidence intervals for clearly\nshowing the difference in vegetation productivity resilience (Figure 3 in the revision, we\ncopied here as Figure B1 above). The statistical significance of our results was tested using\none-tailed Student’s t-tests and Kolmogorov-Smirnov tests at p < 0.05 level. Both methods\nconfirmed that the difference in vegetation productivity resilience to flash and slow drought\nevents at both regions are statistically significant (p < 0.05). This significant difference (p <\n0.05) maintains between different regions but under the same drought types. Additionally,\nbootstrap resampling with 1,000 iterations also confirmed this statistical significance,\nreinforcing the robustness of our findings. Further, we performed our analysis to 0.5° and\n0.25° spatial resolutions, and the consistent results further verified the robustness of our\nfindings (Figure B2). Our analyses using gridded SIF data also produced the consistent\npatterns (Figure B3).\nIn the revision, we clearly added “…one-tailed Student's t-test” in Lines 814.\n14. Figure 4: there aren’t too large of differences between drought and non drought\nhotspots and I am unclear what to takeaway from this other than conclusions about CO .\nYes, we admitted that the difference in individual importance of these factors is hard to\ndistinguish between flash drought hotspots and non-hotspots (Figure S12a in our revision).\nWe highlighted the difference in the responses of vegetation productivity resilience to\ndifferent climate factors, GPP conditions (pre- and during drought period), and rooting\ndepth between flash drought hotspots and non-hotspots based on the partial dependence\nanalyses (Figure S12c-k in our revision). Marked difference exists in the partial\ndependence plots regarding the responses of vegetation productivity resilience to these\nfactors between flash drought hotspots and non-hotspots. We clearly demonstrated and\nhighlighted this difference and the potential mechanism main text in our revision (Lines\n284-300; Lines 309-315). We hope the revision is much clearer and easy to follow.\nMANUSCRIPT REVIEW for \"Increased Spread of Global Flash Drought Threats to\nVegetation Growth Resilience\"\nThe paper, by Guo et al., demonstrates good technical proficiency in data analysis,\neffectively handling multiple datasets to explore flash drought characteristics. However, it\nfalls short in critical areas: the scientific rationale and mechanisms driving the results are\nlargely absent, and the motivation lacks clarity and robustness. Given the significant gaps\nin scientific justification and contribution, I recommend REJECTION, as these issues\nundermine the paper’s overall impact and validity.\nREPLY: We truly appreciate your thorough evaluation and the valuable feedbacks and\ncriticisms. Your comments are extremely helpful for improving our manuscript.\nIn the revision, we thoroughly re-written the introduction, results and discussion sections,\nby performing additional analyses based on your and other reviewers’ comments. The\nmajor revision was summarized below for your convenience.\n1. In the revised introduction, we intensively reviewed the current progress in this research\nfield and clearly demonstrated the scientific rationale and knowledge gap regarding the\nimpacts of flash drought spread on vegetation productivity resilience. The core knowledge\ngap we highlighted here is that we are still unclear regarding to what extent and how will\nthe spatial spread of flash drought events, i.e., rapid spread of flash drought into non-\nhotspots, impact the vegetation productivity resilience.\n2. We streamlined the storyline of our study, and now focused on the divergence in the\nvegetation productivity resilience to flash and slow drought at flash drought hotspots and\nnon-hotspots and the underlying drivers. We highlighted the rapid spread of flash drought\nevents has crucial impacts on vegetation productivity resilience loss and revealed the\nexisting divergence in sensitivities of vegetation productivity resilience to diverse driving\nfactors between flash and slow droughts.\n3. We intensively verified the robustness of our findings using different vegetation\nproductivity proxies, remote sensing retrievals and FLUXNET ground-based observations.\nConsistent patterns were obtained from these validations.\n4. We added a conceptual diagram figure to better illustrate the core idea and scientific\nrationale of this study. This figure makes readers much easier to follow the storyline of this\nstudy and it is well incorporated into the rewritten discussions.\nWe hope the revision clearly demonstrated the current scientific knowledge gap and\ncontributes to the understandings of the impacts of flash drought intensification on\nvegetation productivity resilience loss at global scale.\n1. The manuscript is difficult to follow, and the flow of the discussion is lacking. The\nintegration of slow and flash droughts is unclear, making it hard to understand the\nconnection between the two. Additionally, the novelty of the work is not well-established,\nleaving the importance of the study uncertain. Despite citing 90 articles, the manuscript\nfails to clearly explain the unique contribution of the research. Many of the objectives\naddressed in the study have already been covered in the cited papers. Furthermore, the\ndiscussion of CMIP6 (line 115) seems out of place, as there is no prior explanation of which\nclimate models were selected, how they were chosen, or the methods used for bias\ncorrection and downscaling. Additionally, the process of deriving flash droughts (FDs) is\nnot adequately addressed.\nREPLY: Thank you for your constructive criticisms, and they are extremely helpful for\nimproving our manuscript.\n(1) Restructure of the flow and storyline of this study\nThe storyline of our former version is not well established, and both the results and\ndiscussions are lacking of flow. In the revision, we reorganized the structures of results and\ndiscussions in a more logical way, focusing on the core scientific question. The core\nscientific question we attempted to address is- we still lack a comprehensive understanding\nof the divergence in and the underlying drivers for the vegetation productivity resilience to\nflash and slow droughts at broad scale. The flow of the results is as follows: First, we\nidentified global pattern in flash and slow drought, and discovered a rapid spread of flash\ndrought. Then, we distinguished the divergence in the vegetation productivity resilience to\nflash and slow drought events at two typical regions (flash drought hotspots and non-\nhotspots). Finally, we understand the difference in driving factors underlying\nsuch divergence in vegetation productivity resilience to two types of droughts. We\nreorganized the discussion section accordingly. First, we discussed the intensification and\nspread of flash drought, particularly at non-hotspots. Then, we discussed the lower\nresilience to flash drought and understand the different driving factors underlying the\ndivergence in vegetation productivity resilience to flash and slow drought. Finally, we\nhighlighted that the spread and intensification of flash drought could have devastating\neffects on vegetation productivity resilience loss. We hope the flow of our presentation is\nmuch clearer.\n(2) Clear statement of the novelty and contribution\nIn our former version, the novelty and contribution of this study were not well established.\nIn the revision, we thoroughly rewrote the introduction and clearly presented the core\nknowledge gap in this research field by extensively reviewing the current progress and\nachievements. Increasing studies are investigating the impacts of flash drought on\nvegetation growth and productivity. Albeit these efforts, a comprehensive understanding of\nthe potential divergence and the underlying drivers for the drought resilience of vegetation\n43\nproductivity to slow and flash drought at broad scale remains lacking, which are essential\nand prerequisite for better understanding the devastating effects of flash drought spread\nand intensification (Lines 53-70).\nAdditionally, we added a conceptual diagram figure illustrating the core scientific idea,\nwhich makes the storyline of this study easy to follow and understand (Figure 6 in the\nrevision, we copied here as Figure C1 below).\nFigure C1. Conceptual diagram to illustrate the spread of flash drought and the\nassociated threatened vegetation productivity resilience. There shows an\nintensification and spread of flash droughts, and the climate stress intensifies during this\ntransition. Flash drought reduces the sensitivity of vegetation productivity resilience to CO\n2\nfertilization effects. The resilience of vegetation productivity to flash drought is weaker in\nflash drought hotspots than non-hotspots. Flash drought is spreading rapidly from its\nhotspots to non-hotspots. Larger size of block areas indicates greater effects.\n(3) Detailed explanation of CMIP6\nIn our former version, both the purpose and the detailed information for CMIP6 models are\nnot well described.\nIn the revision, we thoroughly revised these parts. Firstly, we added the detailed description\nfor these models and the selection criteria for model simulations (we added a new Figure\nS14 in the revised supplementary materials, we copied here as Figure C2; Lines 400-413).\nanalyze the future pattern in flash drought events (Figure S9 in the revised supplementary\nmaterials, we copied here as Figure C3; Lines 210-214).\nIn the revision, we have added a detailed explanation of the model selection and\ndownscaling methods in the Method section: “We compiled simulated daily soil\nmoisture (0 – 1 m) data from nine earth system models, which are available in CMIP6\nproject (https://aims2.llnl.gov/search/cmip6/) (O'Neill et al., 2016), to analyze future\nflash drought pattern. Firstly, we chose the SSP245 scenario which is the most likely\nscenario to occur in the future (Sreeparvathy et al., 2022). Secondly, all the models\nare with appropriate spatio-temporal resolution for the future analysis. Thirdly, we\napplied the nearest neighbor method to resample the original simulations into 1\ndegree and took the 5-day soil moisture mean to capture future flash and slow\ndroughts. Fourthly, we evaluated the performance in soil moisture simulation of the\nnine models by Taylor plot (Figure S14 in our revision). On the premise of ensuring\nhigh correlation, we removed the model with lower levels of standard deviation and\nroot mean square deviation (CMCC_CM2_SR5). Finally, we used simulation data of\neight earth system models (mrso; r1i1p1f1; model names: ACCESS-CM2, BCC-\nCSM2-MR, MIROC6, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM\nand NorESM2-MM) of the CMIP6 under the SSP245 scenario (Table S1 in our\nrevision).” (Lines 400-413).\nFigure C2. Selecting of earth system models. A Taylor diagram is utilized to evaluate\nnine Earth system models (ACCESS-CM2, BCC-CSM2-MR, CMCC_CM2_SR5, MIROC6,\nMPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM and NorESM2-MM)\nunder the SSP245 scenario. This assessment is based on three metrics: standard\ndeviation, root mean square deviation, and correlation coefficient, all calculated by\ncomparing each model's output with the average of the nine models.\nflash droughts during 2024 – 2100. (a) Interannual variation in flash drought count\nanomaly from 2024 to 2100. (b) Interannual variation in flash drought severity anomaly\nfrom 2024 to 2100. (c) Interannual variation in flash drought onset speed anomaly from\n2024 to 2100. Simulations of eight CMIP6 earth system models under SSP245 scenario\n(r1i1p1f1; ACCESS-CM2, BCC-CSM2-MR, MIROC6, MPI-ESM1-2-HR, MPI-ESM1-2-LR,\nMRI-ESM2-0, NorESM2-LM & NorESM2-MM) were used to predict the future changes in\nflash drought (Table S1). The linear trends were tested by Mann-Kendall test: *p < 0.05,\n**p < 0.01, ***p < 0.001.\nTable S1. Earth system models (CMIP6) used in this study.\nSource ID Land resolution Variant label Experiment ID Factor\nACCESS-CM2 144 × 192 r1i1p1f1 SSP245 mrso\nBCC-CSM2-MR 160 × 320 r1i1p1f1 SSP245 mrso\nMIROC6 128 × 256 r1i1p1f1 SSP245 mrso\nMPI-ESM1-2-HR 192 × 384 r1i1p1f1 SSP245 mrso\nMPI-ESM1-2-LR 96 × 192 r1i1p1f1 SSP245 mrso\nMRI-ESM2-0 160 × 320 r1i1p1f1 SSP245 mrso\nNorESM2-LM 96 × 144 r1i1p1f1 SSP245 mrso\nNorESM2-MM 192 × 288 r1i1p1f1 SSP245 mrso\n(4) The process of deriving flash droughts is not adequately addressed.\nIn the revision, we thoroughly revised the section “Identification of flash and slow droughts”.\nThe description is now clear. We copied the revision here for your convenient: “We defined\nflash and slow droughts on the basis of soil moisture changes (Yuan et al., 2023). To\ncapture grid-level flash and slow droughts accurately, we used two distinct soil\nmoisture datasets (ERA5_Land and GLDAS_CLSM). The depth-weighted average\nwas calculated for the ERA5_Land soil moisture data (0 – 7 cm, 7 – 28 cm, and 28 –\n100 cm) to get 1-m soil moisture (Yuan et al., 2023). The depth-weighted average soil\nmoisture was calculated using Equation 1:\n𝑺𝑴=(𝟎.𝟎𝟕×𝑺𝑴𝟏+𝟎.𝟐𝟏×𝑺𝑴𝟐+𝟎.𝟕𝟐×𝑺𝑴𝟑) (1)\nSM indicated the weighted mean soil moisture. SM1, SM2 and SM3 indicated soil\nmoisture of 0 – 7 cm, 7 – 28 cm and 28 – 100 cm, respectively. GLDAS_CLSM soil\nmoisture data based on the global land surface model for the top 1 m depth were\nalso used. We then calculated the mean values of the two datasets to reduce the\n46\npotential bias. The mean soil moisture data were applied in the following analyses.\nIn each grid pixel, the soil moisture was aggregated into a 5-day average (pentad),\nwhich was then converted into percentiles after deseasonalization and detrending.\nWe further identified flash and slow droughts during the growing seasons in the\nperiod of 1950 – 2023 (Figure S15 in our revision). The flash drought was identified\nby four rules: (1) the pentad-mean soil moisture decreases from above the 40th\npercentile to the 20th percentile, with an average decline rate of no less than 5\npercentile for each pentad; (2) once the soil moisture drops below the 20th percentile,\nthe flash drought onset ends if the average decline rate is less than 5 percentile per\npentad; (3) if the soil moisture rises above the 20th percentile again, the flash\ndrought terminates; and (4) flash drought lasted at least 4 pentads. Slow drought\nevents were identified as follows: (1) the pentad-mean soil moisture decreases from\nabove the 40th percentile to the 20th percentile, with an average decline rate of less\nthan 5 percentile for each pentad; (2) when the soil moisture drops below the 20th\npercentile, the slow drought onset ends if the soil moisture starts to increase; (3) if\nthe soil moisture rises above the 20th percentile again, the slow drought terminates;\nand (4) slow drought lasted at least 4 pentads.” (Lines 419-443).\nReference:\nO'Neill, B. C., et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci.\nModel Dev. 9, 3461-3482 (2016).\nSreeparvathy, V. & Srinivas, V. Meteorological flash droughts risk projections based on CMIP6\nclimate change scenarios. npj Clim. Atmos. Sci. 5, 77 (2022).\nYuan, X., et al. A global transition to flash droughts under climate change. Science 380, 187-191\n(2023).\n2. The authors have conducted a solid data analysis, demonstrating technical proficiency\nin their approach. However, a significant limitation undermining the paper’s clarity is the\nabsence of justifications or mechanisms explaining the results. While the methods appear\nsound, the paper focuses solely on data analysis without exploring the underlying\nprocesses driving flash drought characteristics. For instance, the claim that energy-limited\nregions exhibit the highest FD ratio, decay, and onset rates is presented without\nsubsequent elaboration on why this occurs, particularly in humid regions, and why\ntransitional regions differ. To strengthen the study, I recommend that the authors delve into\nthe scientific rationale—for example, linking findings to physical drivers like evaporative\ndemand, soil-vegetation feedbacks, or regional climate dynamics—in the discussion.\nREPLY: Thank you for your insightful comments and constructive suggestions.\nIn the revision, we rewrote the discussion section by strengthening the linkage of the results\nand the mechanisms explaining these findings in a much broader context following your\nvaluable suggestions. For example, we expanded extensively the discussion for the finding\nthat higher counts and severity of flash drought occurred in humid regions. We added that\n47\n“The regions with higher flash drought counts also have faster onset speeds and\nhigher severity, which mainly concentrated in humid regions (Figure 1a – c in our\nrevision). Humid regions are characterized by abundant water supply. When there is\nless rainfall during the rainy season, strong solar radiation and high-temperature\nheatwaves can accelerate water loss (Yuan et al., 2023; Christian et al., 2021; Zeng\net al., 2023). Additionally, vegetation in humid regions is typically lush (Zhang et al.,\n2022; Zhao et al. 2025). In facing of water shortage, vegetation can draw a large\namount of moisture from soil layers, which facilitates increased evapotranspiration\nand makes these regions more susceptible to flash drought conditions (Zhang et al.,\n2022; Liu et al., 2023; Chen et al., 2021).” (Lines 185-192).\nNotably, the core scientific question of this study concentrated on the divergence in and\nthe underlying drivers for the vegetation productivity resilience to flash and slow drought.\nWe rewrote and expanded the discussions of the underlying drivers governing the\ndifference in vegetation productivity resilience to flash and slow drought (for example, Lines\n230-232; Lines 241-246; Lines 253-262; Lines 272-281). We reported marked difference\nin sensitivity of vegetation productivity resilience to CO fertilization effect, hydrothermal\n2\nfactors, and the GPP conditions pre- and during drought between the two drought types.\nThese findings improve our understandings of why the spread and intensification of flash\ndrought events exert devastating effects on vegetation productivity resilience.\nReference:\nYuan, X., et al. A global transition to flash droughts under climate change. Science 380, 187-191\n(2023).\nChristian, J. I., et al. Global distribution, trends, and drivers of flash drought occurrence. Nat.\nCommun. 12, 6330 (2021).\nZeng, Z., et al. Increased risk of flash droughts with raised concurrent hot and dry extremes under\nglobal warming. npj Clim. Atmos. Sci. 6, 134 (2023).\nZhang, M., Yuan, X., Otkin, J. A. & Ji, P. Climate warming outweighs vegetation greening in\nintensifying flash droughts over China. Environ. Res. Lett. 17, 054041 (2022).\nZhao, Z., Zhou, Y., Ju, W., Xiao, J. & Li, X. Widespread and divergent post-drought loss of gross\nprimary production. Geophys. Res. Lett. 52, e2025GL115825 (2025).\nLiu, Z., et al. Increased uptake of deep soil water promotes drought resistance in mixed forests.\nPlant, Cell Environ. 46, 3218-3228 (2023).\nChen, L., Ford, T. W. & Yadav, P. The role of vegetation in flash drought occurrence: a sensitivity\nstudy using community earth system model, version 2. J. Hydrometeorol. 22, 845-857 (2021).\n3. The authors introduce the term “slow drought”, but its meaning remains unclear. While\ndroughts are generally understood as slow or creeping phenomena, the paper does not\nspecify whether \"slow drought\" refers to meteorological, hydrological, or soil moisture\ndrought—or if it encompasses all types. When introducing new terminology, particularly in\nthe abstract or introduction, greater care is needed to provide a precise definition. I\nrecommend that the authors clearly define “slow drought” early in the manuscript to avoid\n48\nambiguity and strengthen its scientific grounding.\nREPLY: Thank you for your valuable suggestions.\nBoth flash and slow droughts defined in this study refer to ecological droughts, which focus\non elements like the water supply-demand relationship for vegetation growth and soil\nmoisture characteristics (Jiang et al., 2023; Seneviratne et al., 2021). Soil moisture is\nwidely used to quantify ecological drought occurrence, duration and intensity (Mo et al.,\n2015). When soil moisture declines sufficiently, it stresses vegetation productivity, impairs\ntheir ecophysiological processes, reduces water transport and carbon metabolism, and\npotentially causes plant death (Zhang et al., 2025).\nFollowing your suggestion, we clearly defined “slow drought”, “flash drought” and other\nterms when they first appear. However, there are no single and consistent definition for\nflash and slow drought in literatures. In this study, we defined and distinguished flash and\nslow droughts based on the developing rate. So, the flash droughts refer to the faster-\ndeveloping droughts and slow droughts refer to slower-developing droughts. Therefore, we\nadded: “The flash and slow droughts were determined by the average decline in\nonset speed, with flash drought no less than 5 percentile per pentad and slow\ndrought less than 5 percentile per pentad (see Methods).” (Lines 88-90) and “Flash\ndrought (faster-developing drought)” (Line 22) and “slow drought (slower-\ndeveloping drought)” (Lines 24-25) in the abstract in our revision.\nReference:\nJiang, J. & Zhou, T. Agricultural drought over water-scarce Central Asia aggravated by internal\nclimate variability. Nat. Geosci. 16, 154-161 (2023).\nSeneviratne. Intergovernmental Panel on Climate Change. Working Group I. Climate Change 2021:\nThe Physical Science Basis. IPCC Sixth Assessment Report (2021).\nMo, K. C. & Lettenmaier, P. D. Heat wave flash droughts in decline. Geophys. Res. Lett. 42, 2823-\n2829 (2015).\nZhang, M., et al. A pronounced decline in northern vegetation resistance to flash droughts from\n2001 to 2022. Nat. Commun. 16, 2984 (2025).\n4. The motivation and research objectives of the paper are not strong. The introduction\nstarts well by acknowledging previous flash drought work, but it doesn’t discuss\nmechanisms. Suddenly, the authors bring up the divergence of slow and flash droughts\nwithout properly justifying the research gap. The paper would benefit from a more\nmethodical discussion of why this study matters. Based on the introduction, I didn’t find the\nanalysis or research objectives to be clear. A stronger justification for the study’s purpose\nand contribution would enhance its relevance. I suggest the authors provide a clearer\nexplanation of the significance of their work.\nREPLY: Thank you for your valuable suggestion.\nflash droughts. We copied here for your convenience, “The intensification of flash\ndroughts is mainly attributed to complicated interactions of warmer temperature-\ninduced increased water demands (Zeng et al., 2023), deficit in precipitation supply\n(Yuan et al., 2023; Gu et al., 2025), and the strong land-atmosphere coupling (Qing\net al., 2022; Fu et al., 2023).” (Lines 38-41).\nWe thoroughly rewrote the introduction and strengthened the novelty and research\nobjectives of this study in a broad context of the current progress and achievements in\nrelevant research fields. There are increasing studies investigating the global pattern in\nflash drought occurrence (Yuan et al., 2023) and the associated driving mechanisms\n(Zeng et al., 2023; Fu et al., 2023), the impacts of flash droughts on vegetation growth\n(Zhang et al., 2025; O et al., 2024), the interactions of flash drought and extreme\nheatwaves and their devastating effects on vegetation growth (Chen et al., 2024; Smith\net al., 2022), etc. Albeit these great efforts, we still do not know the divergence in and the\nunderlying drivers for the vegetation productivity resilience to flash and slow drought at\nglobal scale. Comprehensive understanding of this core scientific question is essential and\nprerequisite for better evaluation of the devastating effects of flash drought intensification\nand spread on vegetation productivity resilience and carbon sequestrations.\nIn the revision, we have clearly established the key knowledge gap and our research\nobjectives on the basis of extensive reviewing the previous research achievements (Lines\n48-52; Lines 66-70; Lines 71-83).\nReference:\nZeng, Z., et al. Increased risk of flash droughts with raised concurrent hot and dry extremes under\nglobal warming. npj Clim. Atmos. Sci. 6, 134 (2023).\nYuan, X., et al. A global transition to flash droughts under climate change. Science 380, 187-191\n(2023).\nGu, L., et al. Flash drought impacts on global ecosystems amplified by extreme heat. Nat. Geosci.\n10.1038/s41561-025-01719-y (2025).\nQing, Y., Wang, S., Ancell, B. C. & Yang, Z.-L. Accelerating flash droughts induced by the joint\ninfluence of soil moisture depletion and atmospheric aridity. Nat. Commun. 13, 1139 (2022).\nFu, K. & Wang, K. Contributions of local land-atmosphere coupling and mesoscale atmospheric\ncirculation to the 2013 extreme flash drought and heatwave compound event over Southwest\nChina. J. Geophys. Res.-Atmos. 128, e2023JD039406 (2023).\nZhang, M., et al. A pronounced decline in northern vegetation resistance to flash droughts from\n2001 to 2022. Nat. Commun. 16, 2984 (2025).\nO, S. & Park, S. Global ecosystem responses to flash droughts are modulated by background climate\nand vegetation conditions. Commun. Earth Environ. 5, 1-7 (2024).\nChen, S., et al. Amazon forest biogeography predicts resilience and vulnerability to drought. Nature\n631, 111-117 (2024).\nNat. Clim. Change 12, 477-484 (2022).\n5. The statement at Line 97, claiming that “energy-limited regions show a significantly high\nFD ratio,” does not hold for the Eastern USA, a major energy-limited region. Here, the flash\ndrought (FD) ratio ranges from 10% to 15%, which is notably low rather than high. This\ncontradiction undermines the generalization and requires clarification or revision to reflect\nregional variability accurately. Can the authors explain why this is happening?\nREPLY: Thank you for your valuable comments.\nWe agree with you that there show great variations in flash drought ratios within the same\nclimate region (e.g., energy-limited region), although there exhibits significantly higher flash\ndrought ratio in energy-limited region than water-limited region at the global scale (The\ndifference was significantly different by the one-tailed Student’s t-test at p < 0.05).\nIn the revision, we deleted the results regarding to the spatial pattern (e.g., comparison\nbetween energy- and water-limited regions) in flash drought ratio according to your\ncomments. Instead, we quantified the spatial pattern in the occurrence counts, and the\nabsolute magnitude in onset speed (with unit of percentile pentad-1) and severity (with unit\nof percentile) of flash droughts. We then investigated the distribution of these features in\nthe climate space of mean annual evaporation and mean annual precipitation.\nIn the revision, we revised these results into, “Regions with higher mean annual\nprecipitation and evaporation exhibited greater flash drought counts, more serious\nseverity and faster onset speed (Figure 1d – f in our revision).” (Lines 94-96).\n6. A significant drawback of this paper is the heavy reliance on results presented in the\nsupplementary file, with approximately 70% of the discussion referencing supplementary\nfigures. Supplementary material should support, not dominate, the main text by providing\nadditional context for key arguments. Requiring readers to frequently consult the\nsupplementary file disrupts the flow and accessibility of the narrative. I strongly recommend\nintegrating critical supplementary figures into the main manuscript and discussing them\nthere to improve readability and coherence.\nREPLY: Thank you for your constructive suggestion.\nIn the revision, we reorganized the manuscript to better integrate the key results and the\nassociated discussions. We have moved or integrated some key figures, which are in the\nformer supplementary materials, into the revised figures in main manuscript and ensured\nthese figures are well-contextualized. For example, we integrated the key findings of global\nflash drought hotspots into the new Figure 2 in the main manuscript. These changes could\nenhance the narrative flow and readability.\n51\n7. The definition of flash drought (FD) hotspots is flawed and requires revision. The authors\ndesignate FD hotspots as regions where median values of FD metrics (onset, decay, and\nFD ratio) exceed the global median, inherently classifying roughly 50% of regions as\nhotspots (e.g., 46% of global land area in their results). This statistical approach is\nproblematic, as it dilutes the concept of a “hotspot” by encompassing nearly half the globe,\nrather than pinpointing areas with exceptional FD impact. Hotspots should reflect physically\nmeaningful regions with heightened FD severity—e.g., specific areas like X1, X2, or X3,\nidentified by onset, decay, and intensification rates—rather than a vague percentage of\nland area. I suggest redefining hotspots based on absolute impact criteria and tying them\nto distinct geographic regions for clarity and relevance.\nREPLY: We appreciated your valuable suggestion. We agree with you that our former\ndefinition of flash drought hotspots is flawed.\nIn the revision, we re-defined the flash drought hotspots by using a multivariate flash\ndrought indicator (MFDI) (Mukherjee et al., 2022), which integrates the absolute impacts\nof occurrence counts, onset speed and severity of flash droughts. This MFDI indicator has\nbeen widely used to identify the flash drought hotspots in previous studies (Christian et\nal., 2023; Zeng et al., 2023).\nFirst, we re-calculated the occurrence counts, and the absolute magnitudes in onset speed\nand severity of flash droughts following your valuable suggestion. Then, we calculated the\nmultivariate flash drought indicator (MFDI) using the flash drought counts, absolute onset\nspeed and severity. In the revision, we clearly described the calculation of MFDI as follows\n(Lines 473-486):\n“The MFDI can be calculated as follows:\nLet 𝑿=𝒙 be the matrix with n rows (representing the number of global grids)\n𝒊𝒋\nand m columns (representing flash drought counts, severity, and onset speed) and,\nthe standardized matrix, 𝒁= 𝒛 is then given as:\n𝒊𝒋\n𝒛 =𝟏𝟎𝟎±\n(𝒙𝒊𝒋−𝑴𝒙𝒋 )\n×𝟏𝟎 (2)\n𝒊𝒋\n𝑺𝒙𝒋\nwhere 𝒁 is transformed using a common scale with mean = 100 and\nstandardized deviation = 10, 𝑴 and 𝑺 denote the mean and the standard\n𝒙𝒋 𝒙𝒋\ndeviation of the 𝐣th indicator given as:\n𝑴 =\n∑𝒏\n𝒊=𝟏\n𝒙𝒊𝒋\n(3)\n𝒙𝒋\n𝒏\n𝑺 =√\n∑𝒏\n𝒊=𝟏\n(𝒙𝒊𝒋−𝑴𝒙𝒋 )𝟐\n(4)\n𝒙𝒋\n𝒏\nLet 𝒄𝒗 be the coefficient of variation for the 𝒊th unit:\n𝒊\n𝒄𝒗 =\n𝑺𝒛𝒊\n(5)\n𝒊\n𝑴𝒛𝒊\nwhere 𝑴 =\n∑𝒎\n𝒋=𝟏\n𝒛𝒊𝒋,\n𝑺 =√\n∑𝒎\n𝒋=𝟏\n(𝒛𝒊𝒋−𝑴𝒛𝒊 )𝟐\n𝒛𝒊\n𝒎\n𝒛𝒊\n𝒎\n𝑴𝑭𝑫𝑰=𝑴 ±𝑺 𝒄𝒗 (6)\n𝒛𝒊 𝒛𝒊 𝒊\n”\nWe thus compared the pixel specific mean MFDI to the standardized threshold of 100, and\ndefined the regions with MFDI larger than 100 as the flash drought hotspots (Figure 2 in\nthe revision, we copied here as Figure C4 below). We added the corresponding description\ninto our revision: “We then identified global hotspots of flash drought based on the\nmagnitude of MFDI (see Methods). A higher magnitude of MFDI over 100 indicates\nthat flash drought events are relatively more frequent, severe, and flashy (Figure 2a\nin our revision), and thus regions with MFDI higher than 100 can be considered as\nthe flash drought hotspots (Figure 2b in our revision).” (Lines 115-117). We added\ndetailed description in the methods section (Lines 471-489).\nFigure C4. The spatial pattern of flash drought hotspots and non-hotspots. (a) 3D\nscatter plot showing the association between the flash drought counts, severity, onset\nspeed and multivariate flash drought indicator (MFDI) across study region. (b) MFDI in 29\nregions, arranged in descending order (from left to right) of the mean MFDI magnitude. (c)\nSpatial pattern in flash drought hotspots and non-hotspots. (d) Difference in MFDI between\nflash drought hotspots and non-hotspots. (e) Difference in the mean trends of MFDI during\n1950 – 2023 between flash drought hotspots and non-hotspots. The trends were estimated\nby the Kendall τ method. WLHGra indicates water-limited humid grass. WLHCro indicates\nwater-limited humid crop. WLSHFor indicates water-limited sub-humid forest. ELHFor\nindicates energy-limited humid forest. ELHGra indicates energy-limited humid grass.\nDifferent capital letters in (d) and (e) indicate statistical significance (p < 0.05) in MFDI\nvalue and mean trends between flash drought hotspots and non-hotspots by one-tailed\nReference:\nMukherjee, S. & Mishra, A. K. A multivariate flash drought indicator for identifying global hotspots\nand associated climate controls. Geophys. Res. Lett. 49, e2021GL096804 (2022).\nChristian, J. I., et al. Global projections of flash drought show increased risk in a warming climate.\nCommun. Earth Environ. 4, 165 (2023).\nZeng, Z., et al. Increased risk of flash droughts with raised concurrent hot and dry extremes under\nglobal warming. npj Clim. Atmos. Sci. 6, 134 (2023).\n8. The authors utilize multiple predictors, including precipitation, radiation, soil moisture,\nand available water holding capacity (AWC). I suggest reconsidering the inclusion of AWC,\nas soil moisture already encapsulates sufficient information about water availability and\nstorage capacity. Incorporating AWC as a separate predictor may introduce redundancy,\nand its exclusion could streamline the analysis without compromising the results. I\nrecommend justifying its use or removing it to enhance model efficiency.\nREPLY: In our revision, we carefully re-evaluated the potential collinearity among these\noriginally considered predicting variables by using the VIF criteria (please see Lines 550-\n552). The AWC has a VIF value larger than 5, so we removed it from our final analyses\n(Figure S20a in the revised supplementary materials, and we copied here as Figure C5a\nfor your convenience). We included soil moisture into our final analyses, which already\nreflects water availability and storage.\nFigure C5. Accuracy evaluation of random forest models. (a) Factor collection. (b) The\nresilience of GPP (gross primary productivity) to flash drought at flash drought hotspots.\n54\n(c) The resilience of GPP to slow drought at flash drought hotspots. (d) The resilience of\nGPP to flash drought at flash drought non-hotspots. (e) The resilience of GPP to slow\ndrought at flash drought non-hotspots. We calculated the VIF (Variance Inflation Factor) of\nall factors, and retained the factors with VIF less than 5 for the final attribution analysis.\nWe considered 15 factors in the attribution analyses, including mean temperature during\ndrought response period, mean radiation during drought response period, mean\nprecipitation during drought response period, mean evaporation during drought response\nperiod, mean soil moisture during drought response period, mean vapor pressure deficit\ndrought response period, mean GPP during drought response period, non-growing\nseason snow water equivalent, preceding GPP (mean GPP in two months preceding\ndrought), CO fertilization effect (β), water use efficiency, soil cation exchange\n2\ncapacity, canopy height, maximum root depth, and tree density.\n9. The font sizes in the figures can be increased for better readability.\nREPLY: We have increased the font sizes for all figures in the revision.\n10. In Figure 3, I don’t notice anything significantly different between the hotspot vs. non-hotspot regions. However, the authors state that the results are significantly different across these regions. All the plots in Figure 3 look similar to me. Did the authors perform any statistical significance testing?\nREPLY: The data visualization in our former version is vague and the difference is hard to\ndistinguish.\nIn our revision, we first standardized the vegetation productivity proxies (GPP, SIF) prior to\nour analyses, resulting in a mean of 0 and a standard deviation of 1. This procedure helped\nhighlight differences between flash and slow drought responses. Secondly, we performed\nthe normalization test for vegetation productivity resilience to both flash and slow droughts.\nOur findings showed that these variables meet the normal distribution. Then, we have\nrevised the former boxplot figure to bar charts with 95% confidence intervals for clearly\nshowing the difference in vegetation productivity resilience (Figure 3 in the revision, we\ncopied here as Figure C6 below). The statistical significance of our results was tested using\none-tailed Student’s t-tests and Kolmogorov-Smirnov tests at p < 0.05 level. Both methods\nconfirmed that the difference in vegetation productivity resilience to flash and slow drought\nevents at both regions are statistically significant (p < 0.05). This significant difference (p <\n0.05) maintains between different regions but under the same drought types. Additionally,\nbootstrap resampling with 1,000 iterations also confirmed this statistical significance,\nreinforcing the robustness of our findings. Further, we performed our analysis to 0.5° and\n0.25° spatial resolutions, and the consistent results further verified the robustness of our\nfindings (Figure C7). Our analyses using gridded SIF data also produced the consistent\npatterns (Figure C8). Analysis results derived from Fluxnet site-level observations also\nconfirmed this significant difference (Figure C9).\npattern of mean GPP (gross primary productivity) resilience difference (ΔResilience)\nbetween flash and slow droughts (flash drought minus slow drought). (b) The spatial\npattern of mean GPP resilience to flash drought. (c) The spatial pattern of mean GPP\nresilience to slow drought. (d) Mean GPP resilience to flash and slow droughts within flash\ndrought hotspots and non-hotspots. (e) Mean GPP lag time, resistance and resilience in\ndifferent combinations of flash and slow droughts with flash drought hotspots and non-\nhotspots. Error bars in (d) indicate the 95% confidence intervals of the mean resilience.\nDifferent capital letters in (e) indicate statistical significance (p < 0.05) in resilience to the\nsame drought type between flash drought hotspots and non-hotspots, while different lower-\ncase letters for the same region but different drought types by one-tailed Student’s t-test.\nFigure C7. Difference in GPP resilience to flash and slow droughts at different spatial\nresolutions. (a) Difference in GPP resilience to flash and slow droughts at 1 degree. (b)\nDifference in GPP resilience to flash and slow droughts at 0.5 degree. (c) Difference in\nGPP resilience to flash and slow droughts at 0.25 degree. Error bars indicate the 95%\nconfidence intervals of the mean resilience. Different capital letters indicate statistical\nsignificance (p < 0.05) in resilience between flash drought hotspots and non-hotspots with\nthe same drought type, while different lower-case letters indicate the same region but for\ndifferent drought types by one-tailed Student’s t-test.\npattern of mean SIF (sun-induced fluorescence) resilience difference (ΔResilience)\nbetween flash and slow droughts (flash drought minus slow drought). (b) The spatial\npattern of mean SIF resilience to flash drought. (c) The spatial pattern of mean SIF\nresilience to slow drought. (d) Mean SIF resilience to flash and slow droughts within flash\ndrought hotspots and non-hotspots. (e) Mean SIF lag time, resistance and resilience in\ndifferent combinations of flash and slow droughts with flash drought hotspots and non-\nhotspots. Error bars in (d) indicate the 95% confidence intervals of the mean resilience.\nDifferent capital letters in (e) indicate statistical significance (p < 0.05) in resilience to the\nsame drought type between flash drought hotspots and non-hotspots, while different lower-\ncase letters for the same region but different drought types by one-tailed Student’s t-test.\nFigure C9. Difference in GPP resilience to flash and slow droughts based on Fluxnet\nsites. Error bars indicate the 95% confidence intervals. Different capital letters indicate\nstatistical significance (p < 0.05) between flash drought hotspots and non-hotspots with the\nsame drought type, while different lower-case letters indicate the same region but different\ndrought types by one-tailed Student’s t-test.\nMinor comments\n1. The title is a little difficult to understand since the phrasing “threats vegetation growth\nresilience” is used as a verb. I have an alternative suggestion: “Increased Spread of Global\nGlobal Flash Droughts Threatens Vegetation Growth Resilience.”\nREPLY: Thank you for your valuable suggestion. We have changed the title to “Increased\nSpread of Global Flash Droughts Threatens Vegetation Productivity Resilience”\naccording to your suggestion (Lines 2-3).\n2. Keywords: “Slow drought” is not a key term. Please change it to “drought”.\nREPLY: After carefully checking the recommended article file format on website\n(https://www.nature.com/ncomms/submit/article), we found that key words section is\nunnecessary. Therefore, we deleted the key words section.\n3. Key points (2) and (3) are very large. Please check the allowed maximum length of\ncharacters for the key words for Nature Communications. It’s better to split them into two\nseparate key points.\nREPLY: After carefully checking the recommended article file format on the website\n(https://www.nature.com/ncomms/submit/article), we found that key point section is\nunnecessary for Nature communications. Therefore, we deleted the key points in the\nrevision.\n4. Reference (4) by Satoh et al. is not a flash drought paper but rather a hydrological\ndrought paper. Hence, remove it.\nREPLY: We have deleted this citation.\n5. Line 98: When you say “significantly greater,” did you assess this using a statistical\nsignificance test, or was it just visually interpreted from the figure?\nREPLY: We conducted a statistical significance test using the one-tailed Student’s t-test (p\n< 0.001). We carefully revised relevant sentences in our revision.\n6. Figure 1(a, d, g): Instead of writing “times,” simply write “FD counts.” \"Times\" is not an\ninterpretable word. Be consistent with the figure captions.\nREPLY: Thanks for your suggestions. We have changed “times” to “flash drought counts”\nin Figure 1 in the revision and accordingly revised the figure caption.\n7. Line 449: What do you mean by “cation?”\nREPLY: Here, we used soil cation exchange capacity to indicate soil fertility condition.\nCation exchange capacity refers to the number of exchangeable cations (Mg2+, Na+, Ca2+,\nand K+) in the soil and the capacity for nutritional cation exchange at plant roots caused by\n58\nnegative charges on clay in the soil (Sampatrao et al., 2021). This directly impacts soil\nfertility by determining how well essential nutrients can be retained and made available to\nplants. Soils with high cation exchange capacity can hold more cations, thus supporting\nbetter plant growth and agricultural productivity (Jindo et al., 2014). This soil property has\nbeen widely used in the large-scale studies on the responses of vegetation growth to\nclimate change (Wang et al., 2023; Fang et al., 2017).\nReference:\nSampatrao, G., et al. Review on biomass feedstocks, pyrolysis mechanism and physicochemical\nproperties of biochar: state-of-the-art framework to speed up vision of circular bioeconomy. J.\nClean. Prod. 297, 126645 (2021).\nJindo, K., et al. Review on biomass feedstocks, pyrolysis mechanism and physicochemical\nproperties of biochar: state-of-the-art framework to speed up vision of circular bioeconomy.\nBiogeosciences 11, 6613–6621 (2014).\nWang, X., et al. Enhanced growth resistance but no decline in growth resilience under long-term\nextreme droughts. Global Change Biol. 30, e17038 (2024).\nFang, K., et al. Decreased Soil Cation Exchange Capacity Across Northern China's Grasslands Over\nthe Last Three Decades. J. Geophys. Res.-Biogeo. 30, e17038 (2024).\n8. How did you handle the ERA5 soil moisture data, which are provided at 3 depths? Did\nyou take a weighted average?\nREPLY: We are sorry that our former presentation is not clear.\nYes, we calculated weighted average soil moisture value within 1 m soil depth using\nERA5_Land soil moisture data of the 3 depths (0 – 7 cm, 7 – 28 cm, and 28 – 100 cm),\nensuring that both surface evaporation and root-zone transpiration effects were considered\n(Yuan et al., 2023). We have added the corresponding descriptions in our revision, “The\ndepth-weighted average was calculated for the ERA5_Land soil moisture data (0 – 7\ncm, 7 – 28 cm, and 28 – 100 cm) to get 1-m soil moisture (Yuan et al., 2023).” in Lines\n421-423, and “The depth-weighted average soil moisture was calculated using Equation\n1:\n𝑆𝑀 =(0.07×𝑆𝑀1+0.21×𝑆𝑀2+0.72×𝑆𝑀3) (1)\n” in Lines 423-424.\nReference:\nYuan, X., et al. A global transition to flash droughts under climate change. Science 380, 187-191\n(2023).\nReviewer #1 (Remarks to the Author):\nThe authors did a good job addressing most of my previous comments regarding\nmanuscript #NCOMMS-25-04110A. I appreciate the tremendous effort and substantial\namount of work you have done to put together this revised manuscript. However, the\ngeneral writing could be polished further to achieve a smoother and better flow.\nAnother general comment is about the length/richness of the manuscript. The current\nversion presents a wealth of detail in the results and discussion. Specifically, each of Figs\n1-5 contains more than five panels. Please consider cutting off the context that is not\ndirectly relevant to your objectives.\nFor example, Fig.2c, the map of flash drought hotspots is directly related to your objective\n#1. But Fig.2a, b, and d are not the most important results; they illustrate details of your\nMFDI method and thus should be removed.\nSimilarly, results about GPP response lag time and resistance could also be omitted. Since\nyour objective #2 is to determine the divergence in GPP resilience, the other two are\nsupplementary but not essential.\nAlso, I wonder what the rationale is to devote lengthy space to comparing GPP resilience\nbetween flash drought hotspots vs non-hotspots. Your objective #3 aims to compare GPP\nresilience between flash drought and slow drought. Introducing another dimension (flash\ndrought hotspots vs non-hotspots) complicates the intercomparison, resulting in four RF\nmodels that may distract readers from your core ideas. Please consider deleting Fig.5 and\nthe related text.\nREPLY: Thank you for your valuable comments and suggestions. We reorganized and\npolished the presentation to achieve a smoother and clearer flow according to your\nsuggestion, ensuring the narrative is concise and focused. Specifically, we have further\nstreamlined the Results and Discussions by around 710 words and revised the former five-\npanel Figure 3 into four-panel version. We hope these revisions achieve a better balance\nbetween brevity and completeness.\n(1) Please consider cutting off the context that is not directly relevant to your\nobjectives.\nThank you for your valuable suggestion. Our main research objective is to identify the\ndifference in the resilience of global vegetation productivity to flash and slow droughts.\nTherefore, we have deleted the results related to “resistance” and “lag time” in Figure 3a\nand condensed discussion in the revision.\n2\n(2) The details of MFDI method could be removed from Figure 2.\nWe tended to retain the detailed method of MFDI. The lack of clear definitions, such as\nhow hotspots are defined, in the main text makes it challenging to follow the article (i.e.,\nthe comment of reviewer 2 in the first round). Therefore, we integrated the MFDI method\nfor defining flash drought hotspots in Figure 2 of the revision. We believed that brief details\nof MFDI in the revision is very helpful for readers to improve the readability of the article\nwithout checking the supplement martials.\n(3) GPP response lag time and resistance could also be omitted.\nWe have deleted the related text about “lag time” and “resistance” in the revision.\n(4) Consider deleting related text about comparing GPP resilience between flash\ndrought hotspots vs non-hotspots.\nRespectfully, we retained this part of results. We agree with you that this result is not the\ncore finding. We therefore put it into the discussion section to strengthen the discussion of\n“Intensifying impacts of flash drought on vegetation productivity resilience” with\nFigure S14 and Figure S15, and condensed discussion in the revision. We highlighted flash\ndroughts caused greater reduction in vegetation productivity resilience at flash drought\nhotspots than non-hotspots. This finding supports that the global vegetation productivity\nresilience would persistently deteriorate by intensified flash droughts.\nWe also try to clarify that: (1) Since we compared the difference in vegetation productivity\nresilience to flash and slow droughts at flash drought hotspots and non-hotspots\nrespectively, it was necessary to construct four models (i.e., flash drought hotspots: flash\ndrought and slow drought models, respectively; non-hotspots: flash drought and slow\ndrought models, respectively). (2) Figure 5 in the revision shows the attribution analysis of\nthe difference in the resilience of vegetation productivity to flash and slow droughts at non-\nhotspots. It is not a comparison between hotspots and non-hotspots, and thus needs to be\nretained.\n1. lines 31-33: I can see this is the revised sentence in response to my previous comment\n#2. But the sentence is still not easy to follow currently. What message are you conveying\nhere? Do you mean GPP resilience is more sensitive to flash drought than to slow drought?\nOr did GPP resilience to flash drought decline more than that to slow drought under the\nsame scenario of reduced GPP and intensified climate stress? Also, this finding should be\nplaced right after “...exhibited significantly lower productivity resilience...” in line 29.\nREPLY: We intended to state that compared with slow drought, the vegetation productivity\nresilience exhibited more sensitive to the reduction in vegetation productivity anomaly and\nthe intensification of climate stress caused by flash drought at flash drought hotspots.\nOur former presentation is misleading. We have accordingly revised this sentence into\n“The productivity resilience to flash drought was more sensitive to the same\nreduction in productivity anomaly and intensified climate stress than slow drought.”\nin Lines 29-31 of the revision.\n2. lines 43-46: Please rephrase. For example, you can highlight the different responses of\nGPP to flash vs slow droughts by saying, “Flash droughts may have a more negative effect\non vegetation productivity and carbon sequestration than slow droughts.” This way it also\nbetter connects with the “potential divergence” in lines 48-52.\nREPLY: Thank you for your insightful suggestion. I have rephrased the sentence as follows:\n“Flash droughts have more negative effects on vegetation and carbon sequestration\nthan slow droughts.” (Lines 47-48).\n3. lines 46-48: Could be placed before “The intensification of...” in line 38.\nREPLY: Yes. We have moved sentences “In recent years, rapid intensification of flash\ndroughts has been reported across the United States, Europe, Sahel, China and\nAustralia, that significantly impacted agricultural productivity and terrestrial carbon\nsink.” to Lines 39-42 in the revision, enhancing the logical flow of the manuscript.\n4. lines 51-52: Delete “still”. Delete “and prerequisite”. Replace “predicting the devastating\nconsequences” with “prediction”.\nREPLY: We have accordingly revised this sentence in the revision according to your\nsuggestions.\n5. lines 54-56: Is the rapid decline in soil moisture a cause or result of flash drought?\nThis sentence seems contradictory to your previous and following statements. In lines 56-58,\nyou mentioned “the rapid decline in soil moisture” as a result “in response to flash drought”.\ndeficit in precipitation supply, and the strong land-atmosphere coupling” as the causes of\nflash drought intensification.\nREPLY: The rapid decline in soil moisture is a result of flash drought rather than a cause.\nThe occurrence of flash droughts is strongly coupled with rapid increase in water demands,\nhigh temperatures and extreme radiation levels, which increases evapotranspiration\n(Zhang et al., 2025). When facing flash drought, vegetation uptakes a large amount of\nwater though rooting system, causing the soil moisture to drop rapidly (Ji et al., 2024).\nWe have claimed “In response to flash drought, vegetation quickly loses water into\nthe air, thus exacerbating the rapid decline in soil moisture and plant hydraulic\nfunction (Zhang et al., 2025; Ji et al., 2024).” (Lines 53-55) in the revision.\nReference:\nZhang, M., et al. A pronounced decline in northern vegetation resistance to flash droughts from 2001\nto 2022. Nat. Commun. 16, 2984 (2025).\nJi, P. & Yuan, X. Disparities and similarities in the spatiotemporal dynamics of flash and slow droughts in\nChina. Environ. Res. Lett. 19, 084015 (2024).\n6. lines 60-63: Do you mean slow droughts reduce GPP resilience and affect the\necosystem more than flash droughts? But the focus of your research is flash drought.\nMaybe delete these lines.\nREPLY: We have deleted these sentences in the revision.\n7. lines 66-67: You didn’t mention what exactly the “potential divergence” is. Maybe add\nsomething like “such as higher/lower resilience under flash droughts (citations).”\nREPLY: Thank you for your valuable suggestion. We accordingly changed the sentence\ninto “Nonetheless, the potential divergence in the resilience of vegetation\nproductivity to flash and slow drought remains unknown, such as higher or lower\nresilience under flash droughts (Zhang et al., 2023).” (Lines 59-61) in the revision.\nReference:\nZhang, Y., et al. Spatial heterogeneity of vegetation resilience changes to different drought types. Earth's\nFuture 11, e2022EF003108 (2023).\n8. lines 71-74: “we investigated...observations.” should be placed after “identified flash\ndrought hotspots...” in line 76.\nREPLY: Thank you for your suggestion. We have placed “We investigated the global\n5\npattern and underlying drivers of the responses of vegetation productivity to both\nflash and slow droughts, leveraging on gridded GPP (gross primary productivity)\nand SIF (sun-induced fluorescence) data and Fluxnet site observations.” in Lines 72-\n75 of the revision.\n9. lines 74-76: Maybe rewrite it as “We mapped the count, severity, and onset speed-three\nmetrics of flash and slow droughts globally from 1950 to 2023. Flash drought hotspots were\nthen identified by integrative considering all three metrics.”\nREPLY: We have made modifications according to your suggestion. The revised sentences\nare “Here, we mapped the count, severity, and onset of both flash and slow droughts\nglobally from 1950 to 2023. Flash drought hotspots were then identified by\nintegrating all three metrics (see Methods).” in Lines 70-72 of the revision.\n10. lines 76-78: Maybe delete “the response lag time, resistance and” since you highlighted\n“resilience” in the abstract and discussion.\nREPLY: We have deleted the results and the corresponding discussions of “the response\nlag time and resistance” in the revision following your suggestion.\n11. line 92: Severe flash droughts occurred more in near-equatorial/tropical regions. But\nthe northern part of North America and northern Eurasia do not have a large number of\nflash droughts clustered.\nREPLY: I agree with you totally. After carefully checking, we deleted “the northern part of\nNorth America and northern Eurasia” in the revision.\n12. line 95: Replace “more serious” with “higher”.\nREPLY: Done. (Line 93).\n13. lines 99-102: There should be a sentence to connect flash drought with slow drought.\nDid you try to say “although the severity and onset speed of slow drought also increased\nover time, flash drought occurred more than slow drought globally, especially after the year\n2002”?\nREPLY: Thank you for your constructive suggestion. We have changed the sentence to\n“Although the means severity and onset speed of slow drought also increased\nrapidly (0.01 ± 0.01 percentile yr-1 and 0.01 ± 0.01 percentile / pentad yr-1, both p <\n0.001) (Figure S1 in the supplement material), which were both weaker than flash\ndrought.” (Lines 96-99) in the revision.\n14. lines 103-113: Is it necessary to present the climate anomaly in this section?\nclimate stress compared to slow drought by quantifying the Delta difference (Δ), supporting\nthe lower vegetation productivity resilience to flash drought than slow drought in the\nattribution analysis.\n15. line 114: Add “which integrates three metrics/aspects of the flash drought” after “MFDI”.\nREPLY: We added “which integrates three metrics of the flash drought.” (Line 113) in\nthe revision.\n16. line 116: “flashy” means shining or attractive. Please replace it with something like “fast-\ndeveloped during the onset phase”. Another alternative is to delete lines “A higher\nmagnitude of MFDI...and thus” and also remove Fig 2a, 2d, and 2e.\nREPLY: We replaced “flashy” with “fast-developed during the onset phase” in the\nrevision (Line 115).\n17. lines 120-125: Maybe delete them as they are not directly relevant to your objectives.\nREPLY: Thank you for your insightful suggestion. We believed it was necessary and\nimportant to retain the trend analysis in both flash drought hotspots and non-hotspots in\nthe revision. We tended to express that the globe is under an increasingly severe threat of\nflash drought. The most intuitive evidence is non-hotspots had a stronger tendency of flash\ndrought outbreak compared to flash drought hotspots in all three metrics (i.e., counts, onset\nspeed and severity). It also indicates that flash droughts have a tendency to spread from\nflash drought hotspots to non-hotspots, which is an important finding discussed in\n“Increased Spread of Global Flash Droughts”.\n18. lines 128-136: Consider deleting this paragraph since GPP resilience is your research\nfocus.\nREPLY: We have deleted the paragraph about “lag time” and “resistance” according to\nyour suggestion.\n19. lines 139-140: Replace “concentrated” with “located”. What does “Regions with lower\nGPP resilience to flash drought than that to slow drought” mean? Do you mean “Regions\nwhere GPP resilience declines more after flash drought than slow drought, indicated by\nred colored grids in Fig. 3a”? Try to rephrase it to convey the message.\nREPLY: We replaced “concentrated” with “located” (Line 130) in the revision. Vegetation\nproductivity resilience is the mean state of vegetation anomaly after drought and thus does\nnot decline dynamically. Therefore, we changed the sentence to “Regions with lower GPP\nresilience to flash drought than that to slow drought are mainly located in western\nAmerica, central Asia, eastern Europe and central Australia (Figure 3a – c in the\n7\nrevision).” (Lines 129-131) in the revision.\n20. lines 143-149: Is it necessary to present the result of GPP resilience at flash drought\nnon-hotspots?\nREPLY: We finally retained the GPP resilience results for non-hotspots, because flash\ndroughts are expanding globally, with a more rapid outbreak tendency at non-hotspots than\nflash drought hotspots. Difference in sensitivity of vegetation productivity resilience to the\ndriving factors was prominent in climate factors at flash drought non-hotspots, showing a\nhigher reduction in productivity resilience to flash drought than slow drought along with\nincreased warming and drying stress. This result highlighted the risk of decreasing\nvegetation productivity resilience at non-hotspots increased with the rapid spread of flash\ndrought.\n21. lines 163-164: Do you mean “The positive effect of CO fertilization on GPP resilience\n2\nis stronger for slow droughts than flash droughts”?\nREPLY: Yes. As the effect of CO fertilization effect increases, the resilience of vegetation\n2\nproductivity during slow drought shows a stronger increasing tendency compared to flash\ndrought. However, we chose to express that “… was less sensitive to increasing CO\n2\nfertilization effect” to emphasize the offset effect of flash drought on the CO fertilization\n2\neffect.\nReviewer #1 (Remarks on code availability):\nThe preprocessing codes for producing the aggregated pentad-mean soil moisture are also\nprovided in version 2.\nREPLY: Thank you for your recognition.\nI appreciate the thorough responses from the reviewers. In general, the results and writing\nare much clearer. Many of the quantitative issues and presentations of them that I raised\nin the previous round have been greatly improved. I still have a few concerns about some\nof the results and claims being made throughout. However, I believe they are addressable\ngiven that my concerns now are mostly about argumentation/text and not as much\nquantitative.\nREPLY: Thank you for your valuable re-evaluation of our manuscript and for\nacknowledging the improvements we have made. We appreciated your detailed and\ninsightful comments and carefully addressed each of your specific comments to ensure all\nclaims are clearly articulated, logically supported, and fully consistent with the data\npresented. We hope that the revision could meet the journal’s outstanding standards.\n1) I do appreciate the statistically significant difference for drought resistance. However, for\ndrought resilience, the statistical tests may indicate statistical significance, but the\nmagnitudes tell a different story. I think part of my issue is that the range values in lines\n142-143 are so large, but the ranges are much smaller in the figure. Why is that? These\n0.56 ranges are extremely large and take away from the results, similar to my argument in\nthe first round.\nREPLY: We fully understand your confusion. We standardized the vegetation productivity\ntime series data after deseasonalization and detrending (i.e., grid-level vegetation z-score\ntimes series as a white-noise with a mean of 0 and a standard deviation of 1). The resilience\nwas quantified as the mean of vegetation productivity anomalies after the drought minus\nthe multi-year growing season mean (Figure S19 in supplement material). The resilience\nexhibited the normal distribution that oscillates symmetrically around 0 (Figure B1). A value\nhigher than 0 indicates complete recovery, while a value lower than 0 indicates incomplete\nrecovery. Therefore, the mean state of resilience was shown to be around 0 (i.e., positive\nand negative values cancel each other out). However, in some events vegetation\nproductivity showed extreme low or high resilience to drought events, thus resulting in a\nhigher standard deviation.\nIn the main text, we reported the mean ± standard deviation to indicate the full spread of\nthe underlying distribution, whereas the figures display means and 95% confidence\nintervals to emphasize the resilience mean state and to facilitate visual comparison\nbetween flash and slow droughts at flash drought hotspots and non-hotspots (Figure B2).\nThe 95% confidence interval quantifies the precision of mean, and its width is influenced\nby both the sample standard deviation and the sample size. The narrow 95% confidence\nintervals indicate the mean is precise. The standard deviation and 95% confidence interval\nare complementary rather than contradictory.\nTo accurately express the differences and eliminate misunderstanding, we used Delta to\nexpress the difference in the resilience of vegetation productivity to flash and slow droughts.\nThe corresponding sentences are changed into “The mean GPP resilience to flash\ndrought was significantly higher than that to slow drought (Δ = 0.04 ± 0.01, p <\n0.01) at flash drought hotspots (Figure 3d in the revision). However, an opposite\npattern was observed in GPP resilience (Δ = -0.05 ± 0.02, p < 0.01) at non-hotspots\n(Figure 3d in the revision).” in Lines 131-134 of the revision.\n(a) Probability function of GPP resilience to flash and slow droughts at flash drought\nhotspots. (b) Probability function of GPP resilience to flash and slow drought at non-\nhotspots. Red dash line indicates the mean value of GPP resilience to flash drought, and\nblue dash line indicates the mean value of GPP resilience to slow drought.\nFigure B2. Difference in mean GPP resilience to flash and slow droughts within flash\ndrought hotspots and non-hotspots. Error bars indicate the 95% confidence intervals of\nthe mean resilience. Different capital letters in indicate statistical significance (p < 0.05) in\nresilience to the same drought type between flash drought hotspots and non-hotspots,\nwhile different lower-case letters for the same region but different drought types by one-\ntailed Student’s t-test.\n2) Still a wide expanse of results is being presented without clear connections. For example,\nit is not well motivated how the trends in drought characteristics link to the rest of the\nanalysis. How specifically does the change in flash droughts influence the results in Figure\n3? Are these separate arguments? Potentially, the authors need to draw a better link that\nvegetation is threatened by flash droughts because both the flash droughts are coming\nmore frequent (Figure 1) and vegetation is sensitive to these flash droughts (Figure 3).\nAdditionally, the hotspot versus non-hotspot comparison is still not well motivated either.\nWhy do we need to split these regions? Should there not be specific questions or\nmotivations about these points in the introduction? The objectives are quite broad and\nextensive in lines 80-83 and this leaves open for a wide range of results being presented\nwithout clear connections.\nIn the revision, we streamlined the results by removing results not relevant to the core\nscientific question (including the results of response lag time and resistance), following\nyour comments and suggestions of reviewer #1. We thoroughly reorganized and polished\nthe presentation of the results and the discussions sections, making this manuscript with\na clearer logic flow and the results are now clearly connected.\nIn the discussion section, we had a clear logic flow as following: we first showed that slow\ndrought is translating into flash droughts, while flash droughts are spreading from hotspots\nto non-hotspots. We then discussed the difference in the productivity resilience to flash\ndrought and slow drought. Finally, we proposed that the global vegetation productivity\nresilience would be persistently deteriorated by intensified flash droughts. The\nreorganization of our discussions draws a better link to our results. (Lines 169-295).\nRegarding to the rational for the division of flash drought hotspots and non-hotspots, we\nadded a few sentences in the introduction following your comments, to highlight the\npotential expansion of flash droughts spatiotemporally and the crucial consequences on\nvegetation resilience. We have added the following sentences, “Additionally, a\ncomprehensive understanding of the potential expansion of flash drought\nspatiotemporally remains unconclusive, while previous studies mostly focus on the\nflash drought hotspot regions (Yuan et al., 2023; Chirstian et al., 2021), where flash\ndroughts prevail. We know even less about the potential expansions of flash\ndroughts from flash drought hotspots to non-hotspots (see Methods), and what\nconsequences on vegetation resilience emerge.” in Lines 61-66 of the revision.\nWe also revised the core objectives to “Specifically, the main objectives were to: (1)\ndetermine the divergence in vegetation productivity resilience to flash and slow\ndroughts, and (2) identify the drivers underlying vegetation productivity resilience\nto flash and slow droughts.” in Lines 79-81 of the revision.\nWe hope these revisions made our narrative in a clear logical flow and made our results\nwell connected.\nReference:\nYuan, X., et al. A global transition to flash droughts under climate change. Science 380, 187-191 (2023).\nChristian, J. I., et al. Global distribution, trends, and drivers of flash drought occurrence. Nat. Commun.\n12, 6330 (2021).\n3) Following from #2, it strikes me that the authors’ abstract and title are really saying that\nflash droughts are becoming more common and stronger and that vegetation is sensitive\nto these. I don’t think the authors stay focused making these claims. I find the analysis in\nFigure 1 to be convincing and then the rest of the analysis to be making many claims, many\n12\nof which don’t really get at the central argument here that vegetation will be threatened by\nthese flash droughts. Again, much effort is put into showing how much worse flash droughts\nare for vegetation, but the numbers don’t seem to really support it with ranges 10 times\nhigher than the medians of the resilience’s suggesting a lack of statistically significant\ndifferences. Though, as an aside, a different picture of these ranges is given in Figure 3\nand my guess is the ranges given in text might be something different and my concern is\nnot actually a problem.\nREPLY: Thank you for your insightful comments.\nIn the revision, we removed the results not closely related to our core scientific question.\nOur core study is what drivers the difference in vegetation productivity resilience to flash\nand slow drought, and that intensification of flash drought could deteriorate vegetation\ngrowth resilience. We reorganized the results into the following structure. We first showed\nthat slow drought is translating into flash droughts, and flash droughts are spreading from\nhotspots to non-hotspots. We then investigated the difference in the vegetation productivity\nresilience to flash drought and slow drought. Finally, we investigated the underlying drivers\nfor the divergence in vegetation productivity resilience to flash and slow droughts. The\nrevised results section is following a much clearer logical flow.\nThe productivity data were standardized. The resilience index calculated based on the\nstandardized productivity series clusters around 0, yielding large standard deviations.\nNevertheless, one-tailed Student’s t-test and Kolmogorov-Smirnov test (both p < 0.05)\nverify that vegetation resilience differs significantly between flash and slow droughts within\neach region and between flash drought hotspots and non-hotspots under the same drought\ntype. The details are in the response to main concern 1. The revised sentences are “The\nmean GPP resilience to flash drought was significantly higher than that to slow\ndrought (Δ = 0.04 ± 0.01, p < 0.01) at flash drought hotspots (Figure 3d in the revision).\nHowever, an opposite pattern was observed in GPP resilience (Δ = -0.05 ± 0.02, p <\n0.01) at non-hotspots (Figure 3d in the revision).” in Lines 131-134 of the revision.\nMinor Concerns\n1. Line 78: Resilience and Resistance should be defined somewhere in the main text.\nResilience typically describes the recovery response after the drought period is over.\nResistance is more appropriate to describe responses during the drought.\nREPLY: Thanks for your suggestions. We have added relative descriptions in Lines 76-77\nas follows: “resilience (i.e., the mean of vegetation anomaly after the drought)”. To\nhighlight the core story of the article, we removed the results relating to “Resistance” in\nthe revision.\n2. Lines 96-99: none of the objectives really address this, so why have figures about trends\nin drought characteristics? Should there not be a research question or objective about\n13\ntrends in flash droughts with motivation related to why we are looking at these trends?\nI know this could make sense but explicitly drawing this link is needed.\nREPLY: This part of results aims to illustrate the intensification of flash drought temporally.\nThis result highlights the temporal intensification of flash droughts and provides a solid\nbackground of flash drought intensification for the final part of our discussions, that is the\nintensifying flash droughts could deteriorate vegetation productivity resilience.\nIn order to enhance the clarity of our study, we have revised our main objectives to “(1)\ndetermine the divergence in vegetation productivity resilience to flash and slow\ndroughts, and (2) identify the drivers underlying vegetation productivity resilience\nto flash and slow droughts.” in the revision according to other reviewers’ comments\n(Lines 79-81).\n3. Line 291: I still don’t really know what “hydrothermal” is referring to here. I haven’t seen\nthis word used in the context to discuss plant functioning.\nREPLY: Our former presentation is misleading. The “hydrothermal” was intended to\ndescribe the climate conditions that influence vegetation productivity resilience during flash\ndrought. To avoid the misunderstanding, we have removed “hydrothermal” in the revision.\nReviewer #2 (Remarks on code availability):\nI reviewed the code in the first round and did not need to do so again in this round.\nREPLY: Thank you for your recognition.\nIn this study, Renjie et al. conduct a global-scale assessment of vegetation productivity\nresponses to flash versus slow droughts. The authors integrate multiple datasets, including\nsatellite-derived GPP, FLUXNET site observations, soil moisture reanalysis, and machine-\nlearning–based driver attribution. They report a consistent finding that flash droughts\nreduce productivity more sharply and quickly than slow droughts, with important\nimplications for ecosystem resilience and the global carbon cycle.\nREPLY: We appreciate your positive evaluation on our manuscript. We are grateful for your\ninsightful comments below, which is extremely important for improving our manuscript.\n(1) The authors’ central result-that vegetation productivity is more sensitive under flash\ndroughts (greater reduction, shorter lag time) compared to slow droughts-has already been\nestablished by several earlier studies. Much of the mechanistic framing (e.g., vegetation\ncharacteristics modulating drought sensitivity) is not entirely novel. The authors need to\nclarify what their study contributes beyond confirming existing regional-scale results at the\nglobal level.\nREPLY: Thank you for your critical and insightful comments.\nIncreasing studies have concentrated on the effects and the underlying mechanisms of\nflash droughts on vegetation growth / productivity (Zhang et al., 2025; O et al., 2024). We\nappreciated the improved understandings of the critical consequences on terrestrial\necosystems. Nonetheless, a comprehensive understanding of vegetation productivity\nresilience to both flash and slow droughts is still unclear. More importantly, the drivers that\ngovern the potential divergence in vegetation productivity resilience between flash and\nslow droughts are unestablished.\nThe main innovative findings of this study are summarized into two points. Firstly, we\nuncovered that CO fertilization exerted the greatest individual positive effects on\n2\nproductivity resilience to both flash and slow droughts, and that effect was smaller under\nflash droughts. Secondly, we found that vegetation productivity resilience to flash drought\nwas more sensitive to the anomalies in vegetation growth conditions and climate stresses\nduring flash drought events than slow drought. These findings clearly highlight the\ndifference in the underlying mechanisms governing the vegetation productivity resilience\nto flash and slow droughts.\nReference:\nZhang, M., et al. A pronounced decline in northern vegetation resistance to flash droughts from 2001\nto 2022. Nat. Commun. 16, 2984 (2025).\nO, S. & Park, S. Global ecosystem responses to flash droughts are modulated by background climate and\nvegetation conditions. Commun. Earth Environ. 5, 1-7 (2024).\n(2) The claim that CO fertilization is the dominant global driver, yet vegetation under flash\n2\ndrought is less sensitive to it than under slow drought, is intriguing but insufficiently\nexplained. The causal pathways are not established. The authors should provide a\nstronger scientific rationale for why CO fertilization effects diverge between flash and slow\n2\ndroughts, ideally supported by mechanistic analysis or sensitivity tests.\nREPLY: We appreciate your insightful and constructive suggestion.\nIn the revision, we performed two kinds of analyses to further explain why the CO\n2\n16\nfertilization effects on vegetation productivity resilience were much smaller under flash\ndroughts than slow droughts according to your valuable suggestion.\n(1) We performed the Sobol sensitivity test and confirmed vegetation productivity resilience\nshowed lower sensitivity to CO fertilization effect under flash drought than slow drought\n2\n(0.81 vs 0.86) (Nossent et al., 2011).\n(2) We further performed a random forest model to investigate the important factors\ninfluencing the CO fertilization effects on vegetation productivity resilience under flash\n2\ndroughts and slow droughts. The PDP curves are used to quantify the effects of different\nfactors on CO fertilization effects under both flash and slow droughts (Figure S12 in the\n2\nsupplement material). Here, we copied Figure S12 as Figure C1. Our analyses showed a\nhigher reduction in CO fertilization effect under flash drought than slow drought along with\n2\nincreased warming and drying stress (Figure C1b – g). Vegetation could adopt\nconservative water strategies in response to long-lasting slow and mild drought (He et al.,\n2017). These strategies, together with the CO fertilization effect, will alleviate vegetation\n2\nvulnerability and enhance resilience to persistent and mild drought occurrences (Zhang et\nal., 2025; Li et al., 2023). However, the decrease in vegetation transpiration caused by\nincreasing CO level is largely offset by flash drought-induced rapid water loss (Figure C1b\n2\n– g), probably resulting in fading of the CO fertilization effect (Wang et al., 2023; Marchin\n2\net al., 2022; Xu et al., 2019; Jiao et al., 2021).\nWe accordingly expanded the discussion, “Difference in sensitivity of CO fertilization\n2\neffect was primarily governed by climate factors (Figure S12a in the supplement\nmaterial), showing a higher reduction in CO fertilization effect to flash drought than\n2\nslow drought along with increased warming and drying stress (Figure S12b – g in\nthe supplement material).” in Lines 215-218 of the revision.\ndioxide fertilization effect under flash and slow droughts. (a) The importance of\ndiverse factors for the CO (carbon dioxide) fertilization effect under flash and slow\n2\ndroughts derived from the random forest model, including mean precipitation during\ndrought response period, mean evaporation during drought response period, mean\nradiation during drought response period, mean temperature during drought response\nperiod, mean soil moisture during drought response period, mean vapor pressure deficit\ndrought response period, preceding GPP (mean GPP in two months preceding\ndrought), mean GPP during drought response period, maximum rooting depth, soil cation\nexchange capacity, mean water use efficiency during drought response period, canopy\nheight, non-growing season snow water equivalent, and tree density. (b) Mean\nprecipitation during drought response period. (c) Mean evaporation during drought\nresponse period. (d) Mean radiation during drought response period. (e) Mean temperature\ndrought response period. (f) Mean soil moisture during drought response period. (g)\nMean vapor pressure deficit during drought response period. (h) Preceding GPP. (i) Mean\nGPP during drought response period. (j) Maximum rooting depth. (k) Soil cation exchange\ncapacity. The red and blue points in (a) indicate factor importance for flash and slow\ndroughts, respectively. The red and blue curves in (b) – (k) indicate the response curve of\nCO fertilization effect to each major factor under flash and slow droughts, respectively.\nReference:\nNossent, J., et al. Sobol'sensitivity analysis of a complex environmental model. Environ. Modell. Softw.\n26, 1515-1525 (2011).\nHe, B., Huang, L., Chen, Z. & Wang, H. Weakening sensitivity of global vegetation to long-term droughts.\nSci. China Earth Sci. 61, 60-70 (2018).\nZhang, M., et al. A pronounced decline in northern vegetation resistance to flash droughts from 2001\nto 2022. Nat. Commun. 16, 2984 (2025).\nLi, W., et al. Widespread and complex drought effects on vegetation physiology inferred from space. Nat.\nCommun. 14, 4640 (2023).\nWang, S., et al. Recent global decline of CO fertilization effects on vegetation photosynthesis. Science\n2\n370, 1295-1300 (2020).\nMarchin, R. M., et al. Extreme heat increases stomatal conductance and drought-induced mortality risk\nin vulnerable plant species. Global Change Biol. 28, 1133-1146 (2022).\nXu, C., et al. Increasing impacts of extreme droughts on vegetation productivity under climate change.\nNat. Clim. Change 9, 948-953 (2019).\nJiao, W., et al. Observed increasing water constraint on vegetation growth over the last three decades.\nNat. Commun. 12, 3777 (2021).\n(3) All analyses are conducted at 1° spatial resolution, which is very coarse for capturing\nflash drought dynamics that unfold at subregional scales. The authors should either (a)\ndemonstrate sensitivity of results at finer resolutions where data allow, or (b) explicitly\n18\ndiscuss the limitations of using such coarse spatial aggregation.\nREPLY: We fully understand your concern.\nIn the manuscript, due to the limitation of computing resources we used the data with a\nspatial resolution of 1 degree and the high temporal resolution (pentad, 5 days) to perform\nour analyses, which was used in previous study (Yuan et al., 2023).\nTo validation the robustness of our findings, we repeated our analyses using data at the\nspatial resolution of 0.5 degree and the temporal resolution of 8-days based on the imitated\ncomputing resources. The findings confirmed the consistent pattern in the vegetation\nproductivity resilience to flash and slow droughts (Figure C2b).\nWe confirmed that GPP resilience declined more in response to the same reduction in\nproductivity and intensified climate stress under flash drought than slow drought at 0.5\ndegree (Figure C3; Figure C4). We also confirmed that the CO fertilization effect also\n2\nexerted the greatest positive effect on productivity resilience to both flash and slow\ndroughts, and that effect was smaller under flash droughts (Figure C3a, b; Figure C4a, b)\nat 0.5 degree. Therefore, our findings are consistent at the higher spatial resolution. We\nhave a sentence illustrating the limitation of our study, “Due to the limitation of huge\nlong-term (1950 – 2023) daily data processing, we performed our analyses at a\ncoarse spatial resolution of one degree at the pentad scale following previous study\n(Yuan et al., 2023).” in the revision (Lines 281-283). Notably, this reanalysis results were\nnot included in the manuscript to make the manuscript more concise and readable.\nFigure C2. Difference in GPP resilience to flash and slow droughts at different spatial\nresolutions. (a) Difference in GPP resilience to flash and slow droughts at 1 degree. (b)\nDifference in GPP resilience to flash and slow droughts at 0.5 degree. Error bars indicate\nthe 95% confidence intervals of the mean resilience. Different capital letters indicate\nstatistical significance (p < 0.05) in resilience between flash drought hotspots and non-\nhotspots with the same drought type, while different lower-case letters indicate the same\nregion but for different drought types by one-tailed Student’s t-test.\nresilience to flash and slow droughts at flash drought hotspots based on 0.5 degree.\n(a) The importance of diverse factors for the GPP (gross primary productivity) resilience to\nflash and slow droughts at flash drought hotspots derived from the random forest model,\nincluding β (carbon dioxide fertilization effect), mean GPP during drought response period,\npreceding GPP (mean GPP in two months preceding drought), mean soil moisture during\ndrought response period, mean vapor pressure deficit during drought response period,\nmean temperature during drought response period, maximum rooting depth, mean\nevaporation during drought response period, mean radiation during drought response\nperiod, mean precipitation during drought response period, mean water use efficiency\nduring drought response period, soil cation exchange capacity, canopy height, tree density,\nand non-growing season snow water equivalent. (b) β. (c) Mean GPP during drought\nresponse period. (d) preceding GPP (mean GPP in two months preceding drought). (e)\nMean soil moisture during drought response period. (f) Mean vapor pressure deficit during\ndrought response period. (g) Mean temperature during drought response period. (h)\nMaximum rooting depth. (i) Mean evaporation during drought response period. (j) Mean\nradiation during drought response period. (k) Mean precipitation during drought response\nperiod. The red and blue points in (a) indicate factor importance for flash and slow droughts\nat flash drought hotspots, respectively. The red and blue curves in (b) – (k) indicate the\nresponse curve of GPP resilience to each major factor under flash and slow droughts at\nflash drought hotspots, respectively. The shadows indicate the 95% confidence intervals.\nresilience to flash and slow droughts at non-hotspots based on 0.5 degree. (a) The\nimportance of diverse factors for the GPP (gross primary productivity) resilience to flash\nand slow droughts at flash drought non-hotspots derived from the random forest model,\nincluding β (carbon dioxide fertilization effect), mean GPP during drought response period,\npreceding GPP (mean GPP in two months preceding drought), mean vapor pressure deficit\nduring drought response period, mean precipitation during drought response period, mean\nradiation during drought response period, maximum rooting depth, soil cation exchange\ncapacity, mean temperature during drought response period, mean evaporation during\ndrought response period, mean water use efficiency during drought response period, mean\nsoil moisture during drought response period, non-growing season snow water equivalent,\ntree density, and canopy height. (b) β. (c) Mean GPP during drought response period. (d)\nPreceding GPP. (e) Mean vapor pressure deficit during drought response period. (f) Mean\nprecipitation during drought response period. (g) Mean radiation during drought response\nperiod. (h) Maximum rooting depth. (i) Soil cation exchange capacity. (j) Mean temperature\nduring drought response period. (k) Mean evaporation during drought response period.\nThe red and blue points in (a) indicate factor importance for flash and slow droughts at\nnon-hotspots, respectively. The red and blue curves in (b) – (k) indicate the response curve\nof GPP resilience to each major factor under flash and slow droughts at non-hotspots,\nrespectively. The shadows indicate the 95% confidence intervals.\nReference:\nYuan, X., et al. A global transition to flash droughts under climate change. Science 380, 187-191 (2023).\n(4) Flash drought trends are quantified for 1950 – 2023, whereas vegetation productivity\nresponses (GPP/SIF/FLUXNET) are only analyzed for 2001 – 2019. This mismatch limits\nthe ability to link long-term intensification with ecosystem impacts. The authors should\n21\neither justify why this truncated GPP record is sufficient, or explore proxies/alternative\ndatasets to extend the vegetation analysis over earlier decades.\nREPLY: We understand your concern regarding the mismatch of time series between\nvegetation and drought. We would like to explain in details.\nThe identification of flash and slow droughts was at the pentad scale (5-days), which has\nbeen widely used in previous flash drought studies (Yuan et al., 2023; Yuan et al., 2019;\nZhang et al., 2025). To demonstrate the long-term trend of global flash droughts, we\nselected the daily-scale soil moisture data from 1950 to 2023 and converted them into\npentad-scale for the identification of flash droughts. Relying on 74 years long-term\nidentified flash drought, we found global flash drought is intensifying and spreading.\nHowever, there are few widely used vegetation datasets with a time resolution higher than\n5-days. We selected daily FluxSat and 4-day CSIF datasets which have been widely used\nin previous studies (Kannenberg et al., 2024; Liu et al., 2025). The time span of FluxSat\nis from 2001 to 2019, and that of CSIF is 2001 – 2022. Therefore, we chose the overlapped\nperiod of 2001- 2019 for studying the response of vegetation productivity to drought.\nIt is noteworthy that the daily GPP series can date back before 2001 in a few flux sites of\nthe Fluxnet. We performed the same analyses based on those daily GPP series and found\nconsistent differences in vegetation productivity resilience to flash and slow droughts at the\nsite level (Figure S6 in the supplement material). Thes findings validated the robustness of\nour main findings. We have a sentence accordingly to illustrate the limitation of our study,\n“However, owing to the lack of long-term daily GPP datasets, we only analyzed the\nresponses of vegetation GPP to flash and slow droughts in the period of 2001 – 2019\n(Joiner et al., 2020).” in the revision (Lines 283-285).\nReference:\nYuan, X., et al. A global transition to flash droughts under climate change. Science 380, 187-191 (2023).\nYuan, X., et al. Anthropogenic shift towards higher risk of flash drought over China. Nat. Commun. 10,\n4661 (2019).\nZhang, M., et al. A pronounced decline in northern vegetation resistance to flash droughts from 2001\nto 2022. Nat. Commun. 16, 2984 (2025).\nKannenberg, S. A., Anderegg, W. R., Barnes, M. L., Dannenberg, M. P. & Knapp, A. K. Dominant role of\nsoil moisture in mediating carbon and water fluxes in dryland ecosystems. Nat. Geosci. 17, 38-43 (2024).\nLiu, Z., et al. Enhanced vegetation productivity driven primarily by rate not duration of carbon uptake.\nNat. Clim. Change 15, 560-568 (2025).\nJoiner, J. & Yoshida, Y. Satellite-based reflectances capture large fraction of variability in global gross\nprimary production (GPP) at weekly time scales. Agric. For. Meteorol. 291, 108092 (2020).\n(5) The authors speculate that stronger land-atmosphere coupling amplifies vegetation\nstress during flash droughts, but no direct evidence is presented. This risk reading as\n22\nconjecture. Additional quantitative analysis of land-atmosphere feedbacks (e.g.,\nevaporative fraction anomalies, coupling indices, or case studies of past flash drought\nevents) would greatly strengthen this argument.\nREPLY: We performed additional analyses using the coupling index to illustrate the land-\natmosphere coupling according to your suggestion.\nWe constructed the land-atmosphere coupling index (𝐶𝑆𝐼 ) (Dirmeyer et al., 2011;\n𝑆𝑀−𝑉𝑃𝐷\nYuan et al., 2024). This index can be expressed as follows:\n𝐶𝑆𝐼 =𝑐𝑜𝑟𝑟(𝑆𝑀,𝑉𝑃𝐷)×𝜎(𝑉𝑃𝐷) (1)\n𝑆𝑀−𝑉𝑃𝐷\nwhere 𝑐𝑜𝑟𝑟(𝑆𝑀,𝑉𝑃𝐷) is the Pearson correlation coefficient between the pentad-scale soil\nmoisture and vapor pressure deficit during the drought, 𝜎(𝑉𝑃𝐷) is the standard deviation\nof vapor pressure deficit, and a smaller 𝐶𝑆𝐼 means a stronger land-atmosphere\n𝑆𝑀−𝑉𝑃𝐷\ncoupling state.\nWe calculated global 𝐶𝑆𝐼 from 1950 to 2023. Compared with slow drought, flash\n𝑆𝑀−𝑉𝑃𝐷\ndrought exhibited significantly lower mean negative anomaly in 𝐶𝑆𝐼 index (Δ = -\n𝑆𝑀−𝑉𝑃𝐷\n0.75 ± 1.26, p < 0.001) (Figure C5a), which means global flash drought has greater land-\natmosphere coupling than slow drought. It was worth noting that the global 𝐶𝑆𝐼\n𝑆𝑀−𝑉𝑃𝐷\ncoupling index was showing a significantly decreasing trend (-0.01 ± 0.01, p < 0.001)\n(Figure C5b), which indicates the global coupling state is getting much stronger. Notably,\n𝐶𝑆𝐼 was positively correlated with vegetation productivity resilience (Figure C5c),\n𝑆𝑀−𝑉𝑃𝐷\nindicating that resilience decreases as the coupling strength intensifies.\nWe have added our direct evidences in the discussion, “During flash droughts, the land-\natmosphere coupling strength is stronger than slow droughts (Figure S9a in the\nsupplement material), showing a much rapidly intensifying trend (Figure S9b in the\nsupplement material), and much lower vegetation resilience (Figure S9c in the\nsupplement material) (Pendergrass et al., 2020; Fu et al., 2023; Chen et al., 2025;\nMa et al., 2025).” (Lines 193-195) in the revision. We also supplemented the quantitative\nmethod of 𝐶𝑆𝐼 in Method section (Lines 402-408) in the revision.\n𝑆𝑀−𝑉𝑃𝐷\nFigure C5. Global spatiotemporal pattern in land-atmosphere coupling difference. (a)\nThe spatial pattern in 𝐶𝑆𝐼 difference (flash drought minus slow drought). (b)\n𝑆𝑀−𝑉𝑃𝐷\nInterannual variations in 𝐶𝑆𝐼 during 1950 – 2023. (c) The gradient of GPP\n𝑆𝑀−𝑉𝑃𝐷\nresilience to 𝐶𝑆𝐼 . Shadow in (b) indicated standard deviation. Shadow in (c)\n𝑆𝑀−𝑉𝑃𝐷\n23\nindicated 95% confidence interval. The colored bold lines are the linear fits.\nReference:\nDirmeyer, P. A., et al. The terrestrial segment of soil moisture-climate coupling. Geophys. Res. Lett. 38,\nL16702 (2011).\nYuan, X., et al. Multiscale causes of the 2022 Yangtze mega-flash drought under climate change. Sci.\nChina Earth Sci. 67, 2649-2660 (2024).\nPendergrass, A. G., et al. Flash droughts present a new challenge for subseasonal-to-seasonal prediction.\nNat. Clim. Change 10, 191-199 (2020).\nFu, K. & Wang, K. Contributions of local land-atmosphere coupling and mesoscale atmospheric\ncirculation to the 2013 extreme flash drought and heatwave compound event over Southwest China. J.\nGeophys. Res.-Atmos. 128, e2023JD039406 (2023).\nChen, Z., Qian, Z., Huang, B., Feng, G. & Sun, G. Increased drought impacts on vegetation productivity\nin drylands under climate change. Geophys. Res. Lett. 52, e2025GL115616 (2025).\nMa, F. & Yuan, X. The propagation from atmospheric flash drought to soil flash drought and its changes\nin a warmer climate. J. Hydrol. 654, 132877 (2025).\n(6) The manuscript highlights soil rooting depth and antecedent vegetation growth as\ncritical modulators of resilience. However, these mechanisms are well documented in prior\nliterature. The authors should clearly articulate what is new in their contribution, beyond\nreiterating known processes. The authors can see this paper which is a good paper to\nfollow: “Viewing Soil Moisture Flash Drought Onset Mechanism and Their Changes\nThrough XAI Lens: A Case Study in Eastern China”.\nREPLY: Thank you for sharing with us the interesting and important study (Feng et al.,\n2023).\nWe carefully learned this study and appreciated the novel interpretable machine-learning\nframework revealing that flash-drought onset in China is increasingly co-driven by\nantecedent temperature and vegetation transpiration anomalies, with ~60% of flash\ndrought events linked to cumulative pre-event conditions (Feng et al., 2023). This study\noffers key insights into how preceding vegetation growth conditions modulate the onset of\nflash droughts.\nThis study mainly focuses on the mechanisms underlying the flash drought occurrence\nrather than exploring the detrimental impacts of flash droughts on vegetation productivity\nresilience. In our study, the core scientific question is focused on the driving mechanisms\nunderlying the differences in vegetation productivity resilience to flash and slow droughts,\nby considering diverse factors including the rooting depth and antecedent vegetation\ngrowth conditions. Therefore, the core storylines are totally different between the study of\nFeng et al., 2023 and our study.\nIn the revision, we cited this study to support the discussion regarding to the coupling of\n24\nvegetation growth conditions and flash drought events.\nReference:\nFeng, J., et al. Viewing soil moisture flash drought onset mechanism and their changes through XAI lens:\nA case study in eastern China. Water Resour. Res. 60, e2023WR036297 (2024).\nMinor Comments\n(1) The abstract should be revised for coherence, ensuring that each claim is directly\nsupported by results. In particular, the final sentence feels disconnected from the preceding\ncontent and should be integrated more smoothly or omitted.\nREPLY: We have changed the final sentence to “Our study underscores the limited\nglobal ecosystem resilience to the intensification and spread of flash droughts.” in\nLines 33-34 of the revision.\n25"}]

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