Declining precipitation frequency may drive earlier leaf senescence by intensifying drought stress and enhancing drought acclimation

Domain: earth_science
Published: 2025-01-21
Editorial outcome: minor_revision

Abstract
Precipitation is an important factor influencing the date of foliar senescence, which in turn affects carbon uptake of terrestrial ecosystems. However, the temporal patterns of precipitation frequency and its impact on foliar senescence date remain largely unknown. Using both long-term carbon flux data and satellite observations across the Northern Hemisphere, we show that, after excluding impacts from of temperature, radiation and total precipitation by partial correlation analysis, declining precipitation frequency may drive earlier foliar senescence date from 1982 to 2022. A decrease in precipitation frequency intensifies drought stress by reducing root-zone soil moisture and increasing atmospheric dryness, and limit the photosynthesis necessary for sustained growth. The enhanced drought acclimation, showing a more rapid response to drought, also explains the positive relationship between precipitation frequency and foliar senescence date. Finally, we find 30 current state-of-art Earth system models largely fail to capture the sensitivity of DFS to changes in precipitation frequency and incorrectly predict the direction of correlations for approximately half of the northern global lands, in both historical simulations and future predictions. Our results therefore highlight the critical need to include precipitation frequency, rather than just total precipitation, into models to accurately forecast plant phenology under future climate change.

Introduction
Plant phenology is greatly affected by ongoing changing climate1–3. While spring leaf-out is typically driven by temperature, where warm-ing lead to earlier spring leaf-out4, predicting temporal changes in the dates of autumn foliar senescence (DFS) is more complex due to the multitude of influencing factors. Across northern terrestrial ecosystems, observations and model predictions of DFS are mixed, with both earlier and later senescence reported under different conditions5,6. For example, rising temperatures late in the season can delay DFS given sufficient water availability7. Conversely, warmer conditions can also speed up seasonal development, resulting in earlier DFS8. Moreover, water availability plays a crucial role in autumn phenology, with severe droughts causing earlier DFS9. Thus, understanding the impact of water availability on DFS changes is becoming increasingly vital in the context of climate change10, especially with the expectation of more frequent and severe droughts in the future11,12.

Precipitation is essential for plant growth, particularly through the replenishment of soil moisture13. However, its impact on DFS varies widely, with both positive and negative effects reported. This inconsistency is likely due to local environmental factors, such as the amount of annual precipitation, and geophysical conditions like topography14. The complexity of these patterns and the lack of a clear understanding of the underlying processes make it challenging to accurately model the effects of precipitation on DFS. Previous researches have primarily focused on the total amount of precipitation, but recent studies suggest that it is also essential to consider the temporal patterns of precipitation, such as frequency and intensity, which may have a more pronounced impact on plant phenology15–17. For instance, changes in the frequency of rainfall events, especially prolonged dry periods between events, have been shown to shorten the growth period of various plant species18. In arid and semi-arid ecosystems, shifts in rainfall frequency have been found to significantly alter plant physiological response, such as water uptake efficiency and photosynthesis rates, which could ultimately affect DFS19. A previous study found that leaf onset date is impacted more by precipitation frequency than by its total amount16. This finding is also crucial for understanding DFS, as vegetation tends to be more sensitive to water availability in autumn than in spring20, which would affect ecosystem carbon balance, productivity, and climate feedbacks differently than leaf onset. Therefore, the focus should not be limited to the total precipitation amount but should also encompass its temporal distribution, such as frequency, to better understand its effects on DFS.

Northern hemisphere includes a diverse range of climate zones, vegetation types, and precipitation regimes, making it an ideal region to examine how different precipitation patterns affect DFS. Furthermore, the Northern Hemisphere contains most of the world’s terrestrial vegetation, and phenological changes here significantly impact global biogeochemical cycles and climate systems21. In addition, different plant functional types may respond in diverse ways to changes in precipitation patterns due to their varying physiological traits and adaptations22. Therefore, we aimed to explore the responses and the underlying reasons of DFS to changes in precipitation frequency across the Northern Hemisphere.

In this work, we used DFS derived from long-term flux measurements and satellite observations (Supplementary Fig. 1, Supplementary Data 1–2), combined with precipitation frequency data from gridded meteorological datasets (both ERA5 and CRU) (Supplementary Fig. 2). Additionally, we evaluated the capability of current state-of-the-art Earth system models to reproduce the observed relationships between DFS and precipitation frequency (Supplementary Fig. 3, Supplementary Data 3–4). We show that declining precipitation frequency (Pfreq) is associated with earlier DFS from 1982 to 2022. Reduced Pfreq may exacerbate drought stress and potentially enhance the drought acclimation of plants. Moreover, 30 current state-of-the-art Earth system models fail to accurately represent the sensitivity of DFS to changes in Pfreq, with nearly half of the northern global land area showing incorrect correlation directions in both historical simulations and future projections across various shared socioeconomic pathways.

Results
We observed a widespread decline in Pfreq across the Northern Hemisphere between 1982 and 2022 from both the ERA5 and CRU data (Supplementary Fig. 4). By controlling preseason temperature and radiation through partial correlation analysis, we observed negative correlations between DFS and total precipitation at higher latitudes (>50 degrees), whereas positive correlations between DFS and precipitation were more common at lower latitudes (Fig. 1A). Overall, the proportions of significantly negative and positive DFS-precipitation correlations were 15.6% and 9.5%, respectively. These proportions slightly changed to 17.4% vs. 8.8% when accounting for the effects of precipitation frequency using partial correlation (Fig. 1B). In comparison, Pfreq was mostly positively correlated with DFS, with 57.7% of correlations being positive and 14.9% being significantly positive, about double the proportion of significant negative correlations (7.8%) (Fig. 1C). Further analysis, illustrated in a Sankey diagram, showed that considering the impact of Pfreq significantly reduced the strength of negative DFS-precipitation relationships (Supplementary Fig. 5). This trend remained consistent across different plant functional types (Fig. 1D–F). We also plotted the distributions of the total precipitation-DFS and Pfreq-DFS correlations in the total precipitation and frequency space (Supplementary Fig. 6). We found earlier DFS with increased total precipitation often occurred when precipitation frequency exceeded an empirical threshold of 15. In comparison, the positive correlations between precipitation frequency and DFS were overall broadly consistent, and earlier DFS with increased frequency was observed only for low precipitation frequency but with extreme total precipitation. Flux measurements showed similar patterns (Fig. 1G–I): The correlation between Pfreq and DFS was predominantly positive (23.1% positive vs. 5.7% negative), whereas the correlation between total precipitation and DFS was equally positive and negative (15.8% positive vs. 15.3% negative).

We used a structural equation model (SEM) to explore the underlying mechanisms that may explain the predominantly positive correlation between Pfreq and DFS (Fig. 2A). We found that both total precipitation and Pfreq significantly decreased radiation (path effect of –0.42, –0.43, respectively). In comparison, while both Pfreq and total precipitation positively impacted root zone soil moisture, the influence of Pfreq was stronger (path coefficients of 0.50, P<0.01, and 0.44, P<0.01, respectively). In particular, atmospheric dryness, represented by vapor pressure deficit, increased significantly with declined Pfreq than with total precipitation, with path effects of –0.51 (P<0.01) and –0.35 (P<0.05), respectively. This suggests that declines in precipitation frequency have a more severe impact on plant drought stress than changes in total precipitation, which in turn causes earlier leaf senescence in many regions.

We also found a significantly positive relationship between Pfreq and the drought response lag (R2=0.40, p<0.05), indicating that plants acclimate to drought more quickly with decreased Pfreq, necessitating longer recovery times from drought (R2=0.82, p<0.05, Fig. 2B, C). Using a moving window approach, we observed an increasing importance of Pfreq in regulating DFS changes over the past four decades, indicated by increases in the slope values (Fig. 2D). We further found that decreased Pfreq was often associated with a smaller size of a single rain event (11.7% and 0.9% for positive and negative correlations, respectively), reducing soil moisture accumulation (Fig. 2E) and thereby contributing to the increased soil moisture variability and earlier DFS consequently (R2=0.64, P<0.01, Fig. 2F).

We further tested if the positive impacts of Pfreq on DFS could be reproduced by current state-of-the-art Earth system models. This included both Trendy models for historical simulations and CMIP6 models for future projections under various shared socioeconomic pathways (SSPs), including SSP126, SSP245, SSP370, and SSP585. We found that Trendy models overall captured the relationship between DFS and precipitation frequency, with larger proportions of significant positive correlations (Fig. 3A). Similarly, among the 14 CMIP6 models, only three failed to reproduce the observed patterns (ACCESS-ESM1-5, BCC-CSM2-MR and TaiESM1). However, when assessing the sensitivity of DFS to changes in Pfreq (i.e., how DFS changes per unit variation in Pfreq), we observed substantial differences among models (Fig. 3B). Only seven out of 16 Trendy models demonstrated positive sensitivities, and even fewer CMIP6 models showed positive sensitivities. Additionally, we assessed the accuracy of these models in predicting the sign of the DFS-Pfreq relationship at each pixel level, comparing these predictions with observations (Fig. 3C). About half of all pixels showed mismatches, highlighting the models’ limited accuracy in capturing the DFS-Pfreq correlations.

Methods
Study area
The Northern Hemisphere represents a particularly compelling focus for this study due to its vast and diverse biomes, which range from boreal forests to temperate woodlands and grasslands. This region contains a significant proportion of the world’s terrestrial vegetation and carbon stocks, making it a critical area for studying phenological responses to climate variability. Additionally, the Northern Hemisphere is where some of the most pronounced climate changes, including alterations in precipitation patterns, have been observed in recent decades38. Monitoring the dynamics of vegetation in the Northern Hemisphere is crucial for understanding and mitigating climate. In this study, we focused on middle and high latitude regions of Northern Hemisphere (>30°N), where vegetation dynamic has an evident seasonality. We divided the study area into seven plant functional types according to the biome classification from the terrestrial ecoregions map39, including temperate broadleaf forests (TBF), temperate coniferous forests (TCF), boreal forests (BF), temperature grasslands (TG), montane grasslands (MG), tundra (TD), and xeric shrublands (XS) (Supplementary Fig. 1).

Site-level DFS from flux data
The site-level phenology observations were derived from daily gross primary productivity (GPP) based on the eddy-covariance flux measurements. We removed sites with insufficient observations (<8 yr). As a result, 52 flux sites with a total of 662 year-site records of daily GPP from the FLUXNET database were selected (Supplementary Data 2). The use of GPP to extract phenological stages is a valid approach for observing vegetation phenology, as changes in GPP throughout the year can effectively indicate key phenological events. In this study, we used the end of growing season, derived from GPP time series, to represent the DFS observed at ground level. To obtain smoothed daily GPP curves, we employed a negative exponential approach that utilizes polynomial regression, where weights were assigned based on a Gaussian density function. This method effectively reduces noise in the GPP time series, providing a more accurate representation of the seasonal variations in photosynthetic activity. Subsequently, we defined DFS as the date when daily GPP declined to 10% of the annual maximum GPP40 (Supplementary Fig. 8A).

Satellite derived DFS
The long time series of continuous NDVI dataset from the GIMMS-3G+ product was used to derive large-scale DFS across the Northern Hemisphere (>30°N). This dataset was based on corrected and calibrated measurements from Advanced Very High Resolution Radiometer (AVHRR) data41 with a spatial resolution of 0.0833 degree and a half-month interval for 1982 to 2022.

To better capture the seasonal signals of vegetation while eliminating the interference of atmospheric effects and snow cover, the NDVI time series was fist reconstructed by weighted Whittaker algorithm42. Then a seven-parameter double logistic function43 was used to fit the NDVI time series and DFS was determined based on inflection method44.

f(t) = m1 + m2 * (1 / (1 + e(m3 - t)/m4) - 1 / (1 + e(m5 - t)/m6)) (1)

where, m1 is background NDVI; m2 is the difference between summertime NDVI and background value; m3 and m5 are the midpoints in the days of the year of the transitions of spring green-up and autumn senescence, respectively; m4 and m6 are normalized slope coefficients for these transitions; m7 is summer green-down parameter. DFS was defined as the time when the curvature changing rate reached its last local maximum value (Supplementary Fig. 8B).

Simulated DFS from Trendy and CMIP6
We simulated DFS based on output GPP from 16 Trendy models during 1983–2021 (Supplementary Data 3) and 14 CMIP6 models under different shared socioeconomic pathways (SSPs) during 2016–2100 (Supplementary Data 4). SSPs refer to a set of standardized scenarios developed to facilitate the analysis of climate impacts, adaptation, and mitigation. These pathways describe different trajectories for future global development based on varying assumptions about population growth, economic development, technological advancements, and policy decisions45. For each CMIP6 model, we used the modeled GPP data from four SSPs (SSP-126, SSP-245, SSP-370, and SSP-585) to represent scenarios with low, moderate, high, and very high challenges to mitigation and adaptation, respectively. GPP is closely related to key factors like vegetation coverage, leaf area index, temperature and precipitation, which are crucial in shaping vegetation phenology. Consequently, the annual variation in GPP effectively reflects the phenological stages of vegetation. Based on this theoretical foundation, we first applied cubic spline interpolation to improve data continuity, given that most GPP datasets are available at a monthly temporal resolution. We then fitted the GPP time series using the seven-parameter double logistic function (Eq. 1), defining DFS as the date when the rate of curvature change reached its last local maximum value (Supplementary Fig. 8C).

Climate data
We derived monthly total amount (Ptotal) and frequency (Pfreq) of precipitation from two independent datasets: (1) the Climatic Research Unit Time-Series (CRUTS4.07) and (2) the fifth generation European Center for Medium-Range Weather Forecasts reanalysis of the global climate (ERA5). The CRU dataset is produced by the interpolation from extensive networks of climatic station observations and provides several climate variables on a 0.5°×0.5° spatial resolution and a monthly temporal resolution46. We used wet day frequency, which defined as days with ≥0.1 mm precipitation, as Pfreq. The ERA5 product provides hourly estimates of various climate variables with a spatial resolution of 0.1° based on vast amounts of historical observations47. We obtained total precipitation from the monthly aggregated datasets and calculated the number of rainy days per month based on the daily precipitation (≥0.1 mm). We used the mean value of Ptotal and Pfreq from CRU and ERA5 as final Ptotal and Pfreq for 1982–2022 to reduce the uncertainty from a single dataset. Monthly mean temperature was obtained from CRU and surface net solar radiation was accessed from ERA5. Vapor pressure deficit (VPD) and evapotranspiration (ET) data for 1982–2022 were obtained from TerraClimate with a monthly temporal resolution and a 1/24 degree spatial resolution. The monthly root-zone soil moisture from 1982 to 2022 was obtained from Global Land Evaporation Amsterdam Model (GLEAM) with a spatial resolution of 0.25°.

Identification of drought events, drought recovery and drought response lag
Extreme drought events were identified by examining monthly SPEI-3 (Standardized Precipitation-Evapotranspiration Index at a 3-month scale) values below the threshold of -2. Drought recovery is defined as the duration (months) starting from the month with the deepest suppression of NDVI to the month when NDVI returns to within 95% of the long-term average baseline in each pixel. The monthly SPEI3 and NDVI time series were first smoothed by a 3-month forward moving window, they were then sequentially deseasonalized and linearly detrended. To avoid lengthening the drought recovery duration due to algorithm design, if vegetation recovery extending through the dormant season and into subsequent year, the drought recovery was calculated as the total length of the recovery period minus the length of the dormant season. We measured response lag in months, which is the time between the lowest SPEI3 value and the most significant drop in NDVI caused by drought. We calculated both drought response lag and recovery time for each pixel individually.

Analysis
Precipitation, along with temperature and radiation, collectively regulate DFS48. In addition, covariate effects exist among these climatic variables as well. Therefore, we applied partial correlation analysis to explore the impacts of Ptotal and Pfreq on DFS. Given that the potential biases between flux-based and satellite-based measurements of DFS, we did not combine these two data in the subsequent analyses. Instead, we conducted separate analyses for each measure of DFS to ensure the validity of the results. In the analysis of satellite-based DFS, we used gridded climate data that were resampled using the bilinear interpolation method to match the spatial resolution of the satellite-based DFS. For the flux-based DFS, we directly extracted the corresponding climate data from these gridded datasets based on the coordinates of each flux site. We performed partial correlation analysis under three schemes: (1) partial correlation between DFS and Ptotal, removing the effects of temperature and radiation (scheme 1); (2) partial correlation between DFS and Ptotal, removing the effects of temperature, radiation, and Pfreq (scheme 2); (3) partial correlation between DFS and Pfreq, removing the effects of temperature, radiation, and Ptotal (scheme 3). Preseason forcings have a better predictive strength on phenology than fixed seasonal climate forcing alone4,49. We thus used the preseason mean values of each climatic variable in the partial correlation analysis. For example, the preseason length of Pfreq was defined as the period when the absolute value of partial correlation coefficient between Pfreq and DFS was at its maximum. For each pixel, the preseason periods of 0 to 6 months prior to the multi-year mean DFS were examined (Supplementary Fig. 9).

To investigate the temporal changes in the sensitivity of DFS to Pfreq, we used a moving window method. We conducted tests on a variety of window sizes, ranging from 10 to 20 years. For each window size, we calculated the sensitivity of DFS to Pfreq based on multilinear regression within each moving window. Then we calculated the percentages of significant sensitivity (P<0.05) and fitted these values to obtain the optimal window size with the largest R2. As a result, the optimal window size was set as 19 years to perform subsequent analyses (Supplementary Fig. 10).

DFS = a * Pfreq + b * Ptotal + c * Temperature + d * Radiation + ε (2)

where, a, b, c and d are regression coefficients and represent the sensitivity of DFS to Pfreq, Ptotal, temperature, and radiation, respectively. ε is the residual error. All the climate variables used in the regression were the mean values during preseason.

To explore the potential mechanisms by which precipitation affects DFS, we performed structural equation modeling (SEM), a statistical technique used to test and estimate causal relationships using both direct and indirect paths. We hypothesized that the effect of precipitation patterns on DFS is mediated by their impact on solar radiation and drought conditions. To test this hypothesis, we constructed an SEM that included three mediating variables: radiation, VPD, which indicates atmospheric dryness, and root-zone soil moisture. Path coefficients were estimated using maximum likelihood estimation. Additionally, we assessed the model’s applicability and effectiveness by calculating various statistics and fit indices, such as the chi-square test, the goodness-of-fit index (GFI), and the root mean square error of approximation (RMSEA). The SEM was initially applied to each pixel across the entire study area, and we subsequently selected pixels with a significance level of p<0.05. The mean values of their corresponding coefficients were calculated to determine the final path coefficients.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Overall this is an interesting and important study that demonstrates the negative effect of declining precipitation frequency on DFS\nStrengths: use of 2 meteorological datasets to avoid bias/errors in datasets, uses 2 methods to measure DFS, takes multiple analytical approaches\nWeaknesses: unclear what DFS is, sometimes unclear which data are included in which analyses, unclear what role particular ecosystem/vegetation types play in these patterns\nWhat are the noteworthy results?\n- Precipitation frequency has decreased over the past 40 years and this impacts earlier DFS. This may be explained by increased drought stress, which impacts plant drought responses.\n- Furthermore, earth system models using SSP scenarios failed to capture/predict effects of Pfreq on DFS\nWill the work be of significance to the field and related fields? How does it compare to the established literature? If the work is not original, please provide relevant references.\n- The work is interesting and important to the field.\n- This work is essentially using the methods of: https://www.nature.com/articles/s41558-022-01285-w to examine the date of foliar senescence instead of leaf onset date. The figures, methods, etc. are nearly identical. I don’t necessarily see this as a problem. This study is cited (line 67), but in a very vague way. Could incorporate more and state that you are building on previous studies that have showed that leaf onset date is impacted by Pfreq and that it might also be important for DFS because…\nDoes the work support the conclusions and claims, or is additional evidence needed?\n- Generally, yes, the work supports the conclusions.\nAre there any flaws in the data analysis, interpretation and conclusions? Do these prohibit publication or require revision?\n- It’s unclear what data are used from which dataset (e.g., FLUXNET vs. satellite – is this used for DFS or for precipitation or both?) which inhibits understanding and interpretation. Generally, methods, terms, acronyms are vaguely or not defined which also inhibits understanding.\n- While I don’t think this prohibits publication, it does require revision before publication so that the reader can understand what was done.\nIs the methodology sound? Does the work meet the expected standards in your field?\n- I think so, but it’s hard to tell in the way the manuscript is currently written (see notes above and below)\nIs there enough detail provided in the methods for the work to be reproduced?\n- Do you consider any ecosystem type in the analyses? How might different plant functional groups or ecosystem types influence these analyses? Can you define these (mentioned in Fig 1E and Fig S1, but never explained)? Ecosystem type and plant functional type are not equivalent and this is an important distinction\n- Could you please clarify DFS – both the acronym and the methodology used to assess DFS? My understanding is that DFS is the “Date of Foliar Senescence” and is defined as the date with >50% of the leaves have changed color. As the manuscript is currently written, it is unclear (Line 357) for FLUXNET. How do you define DFS? Are there special considerations across the different ecosystems/plant functional types? How do you compare or combine the two different measures for DFS (FluxNET + satellite)? Which measure of DFS is used in subsequent analyses\n- Consider defining SSPs (socioeconomic pathways) in the methods, or at a minimum, cite them: https://www.sciencedirect.com/science/article/pii/S0959378015000060?via=ihub\n- Why northern hemisphere only?\n- FLUX site data do not go all the way back to 1982, how do you deal with this in your analyses?\nAbstract – Please define DFS as “foliar senescence date” to be consistent with the literature\nLine 29 – how did you show it? Need some statement about methods first\nLine 33 – “enhanced drought acclimation” – what is that?\nLine 43 – “various shared socioeconomic pathways” – can you define this?\nIntroduction\nIntroduction is very limited and does not provide sufficient background information. Why northern hemisphere? How do you define DFS? Why does DFS matter? What have other studies found?\nLine 47-48 – change to “While spring leaf-out is typically driven by temperature, where warming leads to earlier leaf-out, predicting…”\nLine 66 – is it really “emerging”?\nhttps://www.nature.com/articles/s43017-024-00534-0\nhttps://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.12671\nhttps://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2745.13478\nhttps://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.15832\nhttps://link.springer.com/article/10.1007/s10584-012-0622-2\nhttps://onlinelibrary.wiley.com/doi/full/10.1002/ece3.3614\nhttps://www.nature.com/articles/s43017-024-00534-0\nLine 68 – is this type of analysis capable of sorting out the “underlying reasons”? What underlying mechanisms might be driving this change (expanding on this in the introduction would really help the reader)\nLine 70 – what sort of data came from flux and satellite data? DFS?\nResults\nLine 78-79: “By controlling autumn temperature and radiation” – how? Check methods\nLine 102: correct “toal”\nLine 114 : the way this is written it sounds like the p-value refers to a significant difference between the effect of Pfreq and Ptotal on root zone moisture, when actually I think it just means that they are both significant at <0.01. I’d suggest restating this as, “Both Pfreq and total precipitation positively impacted root zone moisture (0.50, P<0.01, and 0.44, P<0.01, respectively).\nLine 115: where is “atmospheric dryness” on this SEM?\nFigures/Tables:\n-check the captions for all tables and figures to ensure that all are thorough and descriptive, using consistent terms across the manuscript (For example, Table S2 needs definitions of the acronyms/column headers)\n-minor comment but I usually associate red with negative\nFigure 1 – consider aligning the 3 columns of the panels and having 3 overall headings (one per column “Precipitation Only”, “Precipitation Frequency included”, “Precipitation frequency only”) to help the reader better follow from top to bottom. I had a tough time understanding that I was supposed to follow those down the panel\nFigure 1E – x-axis label says “Vegetation Type”, caption says “plant functional types (see Methods)” (Line 107), but there is no description of plant functional types in the Methods or anywhere. Please define these acronyms in Fig 1E. I would also suggest some more explicit description about how/why this might be important to your study in the intro, and how plant functional types are defined/measured in the Methods. Differences across plant functional type is mentioned as a key finding in the Discussion.\n• Do the acronyms in Fig1E correspond to Fig 1S? If so, it’s important to clarify that plant functional type and ecosystem type are two very different concepts\nMethods\nMissing description of DFS and how this is defined and compared across the different methods and ecosystems.\nStructural equation modeling needs additional explanation in the methods\nOverall\nNeeds editing from a grammar standpoint – check subject verb-agreement, verb tense etc.", "reviewer_2": "Comments\nThe authors observed a widespread decline in P_freq and found that P_freq is positively correlated to DFS, estimated from long-term carbon flux data and satellite NDVI time series across most of the Northern Hemisphere (NH). The authors concluded that declined P_freq led to earlier DFS, supported by vegetation-drought responses using various data on soil moisture, VPD, and drought response lag. They further pointed out that 16 Trendy models of historical simulation and 14 CMIP6 models with future scenarios all failed to show correct DFS-P_freq relationships.\nThe research topic is very important and interesting to readers. The research is carefully planned, and the manuscript is well-written. I enjoyed reading it.\nSome minor comments and suggestions\n1. The authors preferred to use significance levels instead of actual trend values in trend maps (Fig S3 and S4), which is a bit misleading. A lower significance level, say p=0.01, does not necessarily mean that the corresponding trend is stronger than a trend with a significance level of p=0.05. The significance level depends on the number of samples. I would suggest using trend values in these maps.\nRegarding correlation, I would prefer using a map of correlation coefficients for the same reason. Using the p-value of the correlation indicates the probability of NOT reaching the underlying correlation coefficient, which is useful but does not show the strength or weakness of the correlation between the variables.\n2. Figures 1B and 1C compare unique contributions of total precipitation and precipitation frequency on DFS, after controlling for other drivers. The negative P_total-DFS relationship (59.5%, sig. 17.4%; more total rainfall, earlier DFS) is more prominent than the positive P_freq-DFS relationship (57.7%, sig. 14.9%; more rainy days, later DFS). If we consider that both more total rainfall and more rain days indicate wetter conditions, these two wetter conditions have opposite effects on DFS, with a slightly stronger effect from total precipitation than from P_freq. In this regard, I don’t see precipitation frequency as more important than total precipitation in affecting DFS. Instead, it is merely a trade-off effect on DFS, explaining why total precipitation had no effect on DFS. To me, the most important finding in this research is the strong decreasing trend in P_freq that determined the DFS trend. On the contrary, the perception trend across the NH is not clear and uncertain in future predictions.\n3. The authors tentatively explain the P_freq-DFS relationship using the association of P_freq with vegetation drought response lag. This is not a good explanation in my view. The drought response lag is defined as the duration between the onset of a drought event and the time of maximum suppression of vegetation growth by the event (Li et al., 2023), which is basically related to the drought duration (Mishra and Singh, 2010). The drought duration and the non-rainy days are almost the same thing if drought events are determined using the SPEI index. In this research, the earlier DFS with declining P_freq may be better explained by either stress mechanisms (Sad et al., 2017) or the \"sink limitation\" effect (where plant growth capacity is limited by available resources) via soil moisture availability. The widespread decline in P_freq leads to soil moisture shortage, as the authors demonstrated, consequently reducing sink capacity (i.e. growth capacity regarding water availability), and finally resulting in earlier DFS. This aligns with the view by Zani et al. (2020) that sink limitation governs end-season activities.\nMishra, A.K., & Singh, V.P. (2010). A review of drought concepts. Journal of Hydrology, 391, 202-216\nSade, N., del Mar Rubio-Wilhelmi, M., Umnajkitikorn, K., & Blumwald, E. (2017). Stress-induced senescence and plant tolerance to abiotic stress. Journal of Experimental Botany, 69, 845-853\nZani, D., Crowther, T.W., Mo, L., Renner, S.S., & Zohner, C.M. (2020). Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science, 370, 1066-1071\nMinor typos:\nL97 what is for “PF”\nL101 Fig 1D The three columns in the Sankey diagram are not clear.\nL102 “A represents”\nSupp. L49 GIMMS-3G+ data link https://daac.ornl.gov/VEGETATION/guides/Global_Veg_Greenness_GIMMS_3G.html\nthe authors gave the link for another dataset combining AVHRR and VIIRS of daily steps.\nReviewed by H. Jin, Lund Univ."}, "author_response": "Thank you for both of your nice suggestions and we have revised the manuscript thoroughly\nby a better representation of methods and results (Reviewer 1), as well as a deeper\ndiscussion on the discovered relationship between precipitation frequency and leaf\nphenology in autumn (Reviewer 2). Please find our detailed responses to each of your\nconcerns/suggestions and we thank you again for these valuable contributions that make\nour study more coherent and rigorous.\nReviewer #1 (Remarks to the Author):\nOverall this is an interesting and important study that demonstrates the negative effect of\ndeclining precipitation frequency on DFS\nStrengths: use of 2 meteorological datasets to avoid bias/errors in datasets, uses 2 methods\nto measure DFS, takes multiple analytical approaches\nResponse: Thank you for your positive evaluation and for your comments and suggestions\nthat have helped to substantially improve our study. Our detailed responses to each of your\nsuggestions were listed below and we hope these modifications would be helpful to solve\nyour concerns.\nWeaknesses: unclear what DFS is, sometimes unclear which data are included in which\nanalyses, unclear what role particular ecosystem/vegetation types play in these patterns\nResponse: Thank you pointing out these issues. We have substantially revised our\nmanuscript according to your suggestions.\nWe have explicitly defined and described the methods for extracting DFS from different data\nsources, including flux measurements, satellite observations, and model outputs. Additionally,\nwe have clarified which data were used for each analysis. By incorporating plant functional\ntypes into our analysis, we aim to provide a more comprehensive understanding of the\nresponse of DFS to precipitation and enhance the robustness and generalizability of our\nfindings.\nWhat are the noteworthy results?\n- Precipitation frequency has decreased over the past 40 years and this impacts earlier\nDFS. This may be explained by increased drought stress, which impacts plant drought\nresponses.\n- Furthermore, earth system models using SSP scenarios failed to capture/predict effects\nof Pfreq on DFS\nWill the work be of significance to the field and related fields? How does it compare to the\nestablished literature? If the work is not original, please provide relevant references.\n- The work is interesting and important to the field.\n- This work is essentially using the methods of:\nhttps://www.nature.com/articles/s41558-022-01285-w to examine the date of foliar\nsenescence instead of leaf onset date. The figures, methods, etc. are nearly identical. I don’t\nnecessarily see this as a problem. This study is cited (line 67), but in a very vague way. Could\n1\nincorporate more and state that you are building on previous studies that have showed that\nleaf onset date is impacted by Pfreq and that it might also be important for DFS because…\nResponse: Thank you for pointing out the connection to the study published in Nature\nClimate Change. We appreciate your suggestion and have revised our manuscript to clearly\nstate that our work built on the finding of this previous study. We highlight that\nunderstanding the impact of precipitation frequency on DFS is crucial because vegetation\nmay be more sensitive to water availability in autumn than in spring, influencing ecosystem\ncarbon balance, productivity, and climate feedbacks differently than leaf onset. The\nincorporation of this perspective helps to position our study as an important extension of the\nexisting literature, providing a more comprehensive understanding of how changes in\nprecipitation patterns affect vegetation phenology throughout the growing season. Line\n83-88.\nDoes the work support the conclusions and claims, or is additional evidence needed?\n- Generally, yes, the work supports the conclusions.\nAre there any flaws in the data analysis, interpretation and conclusions? Do these prohibit\npublication or require revision?\n- It’s unclear what data are used from which dataset (e.g., FLUXNET vs. satellite – is this\nused for DFS or for precipitation or both?) which inhibits understanding and interpretation.\nGenerally, methods, terms, acronyms are vaguely or not defined which also inhibits\nunderstanding.\nResponse: Thank you for your valuable feedback. We have revised the manuscript to clarify\nwhat data were used from which dataset. In fact, we did not combine FLUXNET and satellite\ndata in our analyses due to the potential biases between flux-based and satellite-based\nmeasurements of DFS due the difference of scale issues. Instead, we conducted separate\nanalyses for each measure of DFS to ensure the validity of the results. The consistent patterns\nbetween precipitation frequency and DFS among satellite-fluxnet sites would then\nstrengthen the results. Line 515-522.\nWe have also improved the definitions of all methods, terms, and acronyms throughout the\nmanuscript to ensure clarity for understanding.\n- While I don’t think this prohibits publication, it does require revision before publication\nso that the reader can understand what was done.\nIs the methodology sound? Does the work meet the expected standards in your field?\n- I think so, but it’s hard to tell in the way the manuscript is currently written (see notes\nabove and below)\nIs there enough detail provided in the methods for the work to be reproduced?\n- Do you consider any ecosystem type in the analyses? How might different plant\nfunctional groups or ecosystem types influence these analyses? Can you define these\n(mentioned in Fig 1E and Fig S1, but never explained)? Ecosystem type and plant functional\ntype are not equivalent and this is an important distinction\nby the lack of clarity regarding ecosystem types and plant functional types in our manuscript.\nIn our analyses, we consistently used plant functional types throughout the text and figures.\nIn the revised manuscript, we have clarified the source of the plant functional types and\nexplained the corresponding acronyms in the Methods section and figure captions.\nIncorporating plant functional types into our analyses allows us to provide a more\ncomprehensive understanding of how DFS responds to precipitation changes, as different\nplant functional types may exhibit varied responses due to their distinct physiological traits\nand adaptive strategies. Line 96-89, 416-420.\n- Could you please clarify DFS – both the acronym and the methodology used to assess\nDFS? My understanding is that DFS is the “Date of Foliar Senescence” and is defined as the\ndate with >50% of the leaves have changed color. As the manuscript is currently written, it is\nunclear (Line 357) for FLUXNET. How do you define DFS? Are there special considerations\nacross the different ecosystems/plant functional types? How do you compare or combine\nthe two different measures for DFS (FluxNET + satellite)? Which measure of DFS is used in\nsubsequent analyses\nResponse: We are sorry for not clearly defining DFS in the original manuscript. Yes, you are\nright that DFS is the “Date of Foliar Senescence” and is traditionally defined at the plant scale\nas the date with >50% of the leaves have changed color. The use of GPP recorded by\neddy-covariance flux towers to extract phenological stages is also a valid approach for\nobserving vegetation phenology at community scale, as changes in GPP throughout the year\ncan effectively indicate key phenological events. In this study, we used the end of growing\nseason, derived from GPP time series, to represent the DFS observed at ground level. In the\nrevised manuscript, we have provided a detailed description of the method used to extract\nDFS from FLUXNET data, clarifying how these measurements were obtained and applied in\nour analyses. Line 426-436, 439-440, 466-474.\nIn addition, we did not combine FLUXNET and satellite data in our analyses due to the\npotential biases between flux-based and satellite-based measurements of DFS. Instead, we\nconducted separate analyses for each measure of DFS to ensure the validity of the results.\n- Consider defining SSPs (socioeconomic pathways) in the methods, or at a minimum, cite\nthem: https://www.sciencedirect.com/science/article/pii/S0959378015000060?via=ihub\nResponse: Thank you for your suggestion. In the revised manuscript, we have defined the\nShared Socioeconomic Pathways (SSPs) in the Methods section and have cited this reference\nto provide a clearer understanding of the SSPs used in our study. Line 460-464.\n- Why northern hemisphere only?\nResponse: Here is our explanation. The Northern Hemisphere represents a particularly\ncompelling focus for this study due to its vast and diverse biomes, which ranges from boreal\nforests to temperate woodlands and grasslands. This region contains a significant proportion\nof the world’s terrestrial vegetation and carbon stocks, making it a critical area for studying\nphenological responses to climate variability. Additionally, the Northern Hemisphere is\nwhere some of the most pronounced climate changes, including alterations in precipitation\n3\npatterns, have been observed in recent decades. Monitoring the dynamics of vegetation in\nthe Northern Hemisphere is crucial for understanding and mitigating climate. We added\nthese explanations into both the Introduction and Methods sections. Line 60-62, 92-96,\n407-413.\n- FLUX site data do not go all the way back to 1982, how do you deal with this in your\nanalyses?\nResponse: Thanks for this point and we did not combine DFS derived from flux data with\nDFS from satellite data in our analyses. Instead, we conducted separate analyses using DFS\nfrom satellite observations and DFS derived from Flux site observations. This approach helps\navoid uncertainties associated with relying solely on remote sensing data. While the\ntemporal coverage of Flux data does not fully match that of the remote sensing data, our\nresults show that the effects of precipitation frequency on DFS are largely consistent across\nboth data sources. This consistency further supports the robustness of our findings.\nAbstract – Please define DFS as “foliar senescence date” to be consistent with the literature\nResponse: We modified the definition of DSF as “date of foliar senescence” according to\nyour suggestion. Line 34-35.\nLine 29 – how did you show it? Need some statement about methods first\nResponse: We show that declining precipitation frequency drives earlier DFS after excluding\nimpacts of temperature, radiation and total precipitation by partial correlation analysis. We\nadded the method, specifying the use of partial correlation analysis, to the relevant section\nof the Abstract. Line 38-39.\nLine 33 – “enhanced drought acclimation” – what is that?\nResponse: In our study, we found plants experiencing decreased P showed a more rapid\nfreq\nresponse to drought, as represented by a shorter drought response lag, a measure of the\ntime between a drought event and the most severe reduction in vegetation growth. This\nprocess could represent drought acclimation and enhanced drought acclimation means that\nplants respond more quickly to drought conditions.\nLine 43 – “various shared socioeconomic pathways” – can you define this?\nResponse: We added the definition of “various shared socioeconomic pathways” in the\nMethods section. Line 460-464.\nIntroduction\nIntroduction is very limited and does not provide sufficient background information. Why\nnorthern hemisphere? How do you define DFS? Why does DFS matter? What have other\nstudies found?\nResponse: Thank you for pointing out these issues. We have expanded the Introduction\nsection to provide more comprehensive background information. We have clarified the\n4\ndefinition of DFS, explained the importance of focusing on DFS, and justified the selection of\nthe Northern Hemisphere as the study area. Additionally, we have included a summary of\nrelevant findings from other studies to provide better context for our research. Line 76-98.\nLine 47-48 – change to “While spring leaf-out is typically driven by temperature, where\nwarming leads to earlier leaf-out, predicting…”\nResponse: We have revised this sentence as you suggested. Line 57-60.\nLine 66 – is it really “emerging”?\nhttps://www.nature.com/articles/s43017-024-00534-0\nhttps://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.12671\nhttps://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2745.13478\nhttps://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.15832\nhttps://link.springer.com/article/10.1007/s10584-012-0622-2\nhttps://onlinelibrary.wiley.com/doi/full/10.1002/ece3.3614\nhttps://www.nature.com/articles/s43017-024-00534-0\nResponse: We revised the inappropriate use of the term “emerging”. In the revised\nmanuscript, we have summarized and cited these relevant studies to better highlight the\nfocus and significance of our research. Line 76-88.\nLine 68 – is this type of analysis capable of sorting out the “underlying reasons”? What\nunderlying mechanisms might be driving this change (expanding on this in the introduction\nwould really help the reader)\nResponse: Thanks for your suggestion. In the revised Introduction, we have expanded on\nthe potential mechanisms driving the observed changes. We proposed that precipitation\naffects DFS by altering water availability and light condition, which could influence\nvegetation water uptake efficiency and photosynthetic rates, ultimately impacting phenology.\nTo explore these underlying mechanisms, our subsequent analyses incorporate factors such\nas solar radiation, vapor pressure deficit (VPD), root-zone soil moisture, and the lag and\nrecovery times of vegetation responses to drought. Line 81-83.\nLine 70 – what sort of data came from flux and satellite data? DFS?\nResponse: Yes, the data obtained from both flux and satellite sources is DFS. We have\nclarified this in the revised manuscript. Line 100-101.\nResults\nLine 78-79: “By controlling autumn temperature and radiation” – how? Check methods\nResponse: We have changed to “By controlling preseason temperature and radiation\nthrough partial correlation analysis”. Line 110.\nLine 102: correct “toal”\nResponse: We have corrected this typo. Line 136.\nbetween the effect of Pfreq and Ptotal on root zone moisture, when actually I think it just\nmeans that they are both significant at <0.01. I’d suggest restating this as, “Both Pfreq and\ntotal precipitation positively impacted root zone moisture (0.50, P<0.01, and 0.44, P<0.01,\nrespectively).\nResponse: We have revised this statement as you suggested to avoid miss understanding.\nLine 149-151.\nLine 115: where is “atmospheric dryness” on this SEM?\nResponse: In the SEM, “atmospheric dryness” is represented by the vapor pressure deficit\n(VPD) and we have clarified this in the revised manuscript. Line 151-152.\nFigures/Tables:\n-check the captions for all tables and figures to ensure that all are thorough and descriptive,\nusing consistent terms across the manuscript (For example, Table S2 needs definitions of the\nacronyms/column headers)\nResponse: We have carefully checked the captions for all tables and figures to ensure they\nare thorough and descriptive, and we have used consistent terms throughout the\nmanuscript.\n-minor comment but I usually associate red with negative\nResponse: We modified the colors of the figures, using red to indicate negative values.\nFigure 1 – consider aligning the 3 columns of the panels and having 3 overall headings (one\nper column “Precipitation Only”, “Precipitation Frequency included”, “Precipitation\nfrequency only”) to help the reader better follow from top to bottom. I had a tough time\nunderstanding that I was supposed to follow those down the panel\nResponse: We apologize for the difficulty in understanding the Figure 1. According to your\nsuggestion, we revised this figure. To make it clearer, we have aligned the three columns of\npanels and added overall headings—Scenario 1, Scenario 2, and Scenario 3—to each\ncolumn. Additionally, we have included detailed explanations for each scenario in the figure\ncaption.\nFigure 1E – x-axis label says “Vegetation Type”, caption says “plant functional types (see\nMethods)” (Line 107), but there is no description of plant functional types in the Methods or\nanywhere. Please define these acronyms in Fig 1E. I would also suggest some more explicit\ndescription about how/why this might be important to your study in the intro, and how plant\nfunctional types are defined/measured in the Methods. Differences across plant functional\ntype is mentioned as a key finding in the Discussion.\nResponse: In the revised manuscript, we have used the term “plant functional types”\nconsistently throughout the figures and text. We have defined the related acronyms in the\nfigure captions and provided additional descriptions of the plant functional types in both the\nIntroduction and Methods sections.\n6\n• Do the acronyms in Fig1E correspond to Fig 1S? If so, it’s important to clarify that plant\nfunctional type and ecosystem type are two very different concepts\nResponse: Yes, the acronyms for plant functional types in Figure 1 correspond to those in\nFigure S1. To make it clarity, we have explained the acronyms in the caption of Figure 1.\nMethods\nMissing description of DFS and how this is defined and compared across the different\nmethods and ecosystems.\nResponse: We have improved the Methods section to clearly define and explain how DFS is\nextracted from the different datasets. Line 426-436, 439-455, 466-474. Additionally, to\nmake it more intuitive, we have added a figure of the DFS extraction process in the\nsupplementary materials (Figure S5).\nFigure S5 A schematic diagram illustrating the extraction of the date of foliar senescence\n(DFS) from flux-observed GPP (A), satellite-observed NDVI (B), and simulated GPP from\nTrendy/CMIP6 models (C).\nStructural equation modeling needs additional explanation in the methods\nResponse: Thank you for your suggestion. In the revised manuscript, we have provided\nadditional explanations in the Methods section regarding the structural equation modeling\n(SEM). We have elaborated on the rationale behind using SEM, the specific variables\nincluded in the model, and the steps taken to perform the analysis, including assumption,\nestimation methods, and model evaluation criteria. Line 547-559\nOverall\nNeeds editing from a grammar standpoint – check subject verb-agreement, verb tense etc.\nResponse: We have carefully checked the language to avoid any grammatical errors.\nReviewer #2 (Remarks to the Author):\nComments\nThe authors observed a widespread decline in P_freq and found that P_freq is positively\ncorrelated to DFS, estimated from long-term carbon flux data and satellite NDVI time series\n7\nacross most of the Northern Hemisphere (NH). The authors concluded that declined P_freq\nled to earlier DFS, supported by vegetation-drought responses using various data on soil\nmoisture, VPD, and drought response lag. They further pointed out that 16 Trendy models of\nhistorical simulation and 14 CMIP6 models with future scenarios all failed to show correct\nDFS-P_freq relationships.\nThe research topic is very important and interesting to readers. The research is carefully\nplanned, and the manuscript is well-written. I enjoyed reading it.\nResponse: Thank you very much for your positive comments on our manuscript. According\nto your comments and suggestions, we have made extensive modifications to our\nmanuscript.\nSome minor comments and suggestions\n1. The authors preferred to use significance levels instead of actual trend values in trend\nmaps (Fig S3 and S4), which is a bit misleading. A lower significance level, say p=0.01, does\nnot necessarily mean that the corresponding trend is stronger than a trend with a\nsignificance level of p=0.05. The significance level depends on the number of samples. I\nwould suggest using trend values in these maps.\nRegarding correlation, I would prefer using a map of correlation coefficients for the same\nreason. Using the p-value of the correlation indicates the probability of NOT reaching the\nunderlying correlation coefficient, which is useful but does not show the strength or\nweakness of the correlation between the variables.\nResponse: Thank you for this valuable comment. According to your suggestion, we revised\nthe trend maps (Figure S3 and S4) and partial correlation coefficient maps (Figure 1). In these\nmaps, we now directly display the trend values or correlation coefficients and use dots to\nindicate regions with a significance level of p<0.05.\n2. Figures 1B and 1C compare unique contributions of total precipitation and precipitation\nfrequency on DFS, after controlling for other drivers. The negative P_total-DFS relationship\n(59.5%, sig. 17.4%; more total rainfall, earlier DFS) is more prominent than the positive\nP_freq-DFS relationship (57.7%, sig. 14.9%; more rainy days, later DFS). If we consider that\nboth more total rainfall and more rain days indicate wetter conditions, these two wetter\nconditions have opposite effects on DFS, with a slightly stronger effect from total\nprecipitation than from P_freq. In this regard, I don’t see precipitation frequency as more\nimportant than total precipitation in affecting DFS. Instead, it is merely a trade-off effect on\nDFS, explaining why total precipitation had no effect on DFS. To me, the most important\nfinding in this research is the strong decreasing trend in P_freq that determined the DFS\ntrend. On the contrary, the perception trend across the NH is not clear and uncertain in\nfuture predictions.\nResponse: Thank you for this important comment and we fully agree with your perspective\non the trade-off effect between precipitation frequency and total precipitation on DFS. In the\nDiscussion section, we added a description of this trade-off effect and its implications for\npredicting future ecosystem functions in response to climate change. Line 236-241.\n3. The authors tentatively explain the P_freq-DFS relationship using the association of P_freq\n8\nwith vegetation drought response lag. This is not a good explanation in my view. The\ndrought response lag is defined as the duration between the onset of a drought event and\nthe time of maximum suppression of vegetation growth by the event (Li et al., 2023), which is\nbasically related to the drought duration (Mishra and Singh, 2010). The drought duration\nand the non-rainy days are almost the same thing if drought events are determined using\nthe SPEI index. In this research, the earlier DFS with declining P_freq may be better explained\nby either stress mechanisms (Sad et al., 2017) or the \"sink limitation\" effect (where plant\ngrowth capacity is limited by available resources) via soil moisture availability. The\nwidespread decline in P_freq leads to soil moisture shortage, as the authors demonstrated,\nconsequently reducing sink capacity (i.e. growth capacity regarding water availability),\nand finally resulting in earlier DFS. This aligns with the view by Zani et al. (2020) that sink\nlimitation governs end-season activities.\nMishra, A.K., & Singh, V.P. (2010). A review of drought concepts. Journal of Hydrology, 391,\n202-216\nSade, N., del Mar Rubio-Wilhelmi, M., Umnajkitikorn, K., & Blumwald, E. (2017).\nStress-induced senescence and plant tolerance to abiotic stress. Journal of Experimental\nBotany, 69, 845-853\nZani, D., Crowther, T.W., Mo, L., Renner, S.S., & Zohner, C.M. (2020). Increased\ngrowing-season productivity drives earlier autumn leaf senescence in temperate trees.\nScience, 370, 1066-1071\nResponse: Thanks for this important suggestion and we agree with your suggestions on the\nperspective of “sink limitation”. In fact, our results shown in Figure 3F are exactly in line with\nthe “sink limitation” by the soil moisture effects. A slight difference is that we used the\ncoefficient of variation (CV, %) of soil moisture instead of its absolute values. This is because\nthe CV allows the comparison of soil moisture across different ecoregions and plant\nfunctional types. Large changes in CV of soil moisture indicate the harsh environment for\nplant growth (Knapp et al., 2002) and in Figure 3F we clearly showed that larger CV is\ncoincident with earlier DFS, so that the idea of “sink limitation” could be indirectly\ndemonstrated. We added explanations in the discussion section. Line 241-247.\nMinor typos:\nL97 what is for “PF”\nResponse: We are sorry for this typo and correct it to “P ”. Line 129.\nfreq\nL101 Fig 1D The three columns in the Sankey diagram are not clear.\nResponse: We have separated the Sankey diagram from the original Figure 1 and made it as\nthe Extended Figure 1. In addition, we have clarified what each column represents in the\ndiagram to enhance clarity.\nL102 “A represents”\nResponse: We have corrected this error.\nSupp. L49 GIMMS-3G+ data link\n9\nhttps://daac.ornl.gov/VEGETATION/guides/Global_Veg_Greenness_GIMMS_3G.html\nthe authors gave the link for another dataset combining AVHRR and VIIRS of daily steps.\nResponse: We have corrected the link to\nhttps://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2187\nReviewed by H. Jin, Lund Univ.\n10"}, {"round": 2, "reviewer_comments": {"reviewer_2": "Thank you for the updated version. My concerns have all been addressed. However, there are a few minor comments and some clerical misspellings that need a further check by the authors.\nL142-143 “G–F shows … ”, should be “G-I show…” . I only see a dot line on the plot, on dotted area. Please have a second check\nL169 Figure 2 B and C: I suggest switching the x and y axes for these two plots, as you're showing how changes in Pfreq lead to changes in drought response times (lag and recovery). It would be clearer if you explained it like this: a 1-day increase in Pfreq per month will, on average, result in a 14.3-month increase in drought response lag (the inverse of the slope, 0.07) and a 6.7-month decrease in recovery time. I understand there is considerable variation in these violin plots depending on the timing of the drought and the plant's growth stage. Also, please double-check the unit of the slope in your plots B and C: are they in days per month? The inverse value of 0.07 gives 14.3 months of increasing response lag with a 1-day increase in precipitation frequency, probably unrealistic to me.\nL35-37 Supplement: Please double-check the unit of Pfreq in days/year in Figure S4. You are using monthly Pfreq, so it's important to clearly describe the slope in terms of monthly days/year or a similar unit. Otherwise, the small trend values in days/year per month could be mistaken for annual days/year. L37 “yarly” should be “yearly”", "reviewer_1": "Comments to the authors response to Reviewer 1:\nThe authors have addressed all the comments by reviewer 1. However, one comment about that the current study is based on Wang et al. (2022, Nature Climate Change), the authors could elaborate more in discussion.\nThe authors addressed Reviewer 1’s comments regarding the methodology and research concept, which build on the paper by Wang et al. (2022, Nature Climate Change) that examined the effect of P_frq on spring phenology. The distinct contribution of the current manuscript could be highlighted further (perhaps in the Discussion), for the following reasons:\nWang et al. (2022) demonstrated the importance of P_frq on leaf onset timing in spring, supplementing the well-known dominant role of temperature on spring phenology. However, the internal and external signals that trigger tree dormancy release remain unclear (Singh et al. 2017; Yumoto, et al. 2024). In this study, we reveal the controlling effect of P_frq on autumn leaf senescence, a factor that has been largely overlooked ...\nSingh, R. K., et al. (2017). \"Photoperiod- and temperature-mediated control of phenology in trees – a molecular perspective.\" New Phytologist 213(2): 511-524.\nYumoto, G., et al. (2024). \"Seasonal switching of integrated leaf senescence controls in an evergreen perennial Arabidopsis.\" Nature Communications 15(1): 4719.\nOther minor comments\nL142 “G-F” should be “G-I”\nAnother minor comment is about the term “Scenario”. The three Scenarios by controlling different drivers in Figure 1 might confuse readers with the CIMPS SSP scenarios.\nConsidering change the scenario term in Figure 1 and corresponding text, for example “Scheme”, “Condition”, “Situation”, “Profile”, or other similar one that fits here best."}, "author_response": "Thank you for both of your further suggestions and we have revised the manuscript\nthoroughly. Please find our detailed responses to each of your concerns/suggestions\nand we thank you again for these valuable contributions that make our study more\ncoherent and rigorous.\nComments to the authors response to Reviewer 1:\nThe authors have addressed all the comments by reviewer 1. However, one comment about\nthat the current study is based on Wang et al. (2022, Nature Climate Change), the\nauthors could elaborate more in discussion.\nThe authors addressed Reviewer 1’s comments regarding the methodology and research\nconcept, which build on the paper by Wang et al. (2022, Nature Climate Change) that\nexamined the effect of P_frq on spring phenology. The distinct contribution of the\ncurrent manuscript could be highlighted further (perhaps in the Discussion), for\nthe following reasons:\nWang et al. (2022) demonstrated the importance of P_frq on leaf onset timing in spring,\nsupplementing the well-known dominant role of temperature on spring phenology.\nHowever, the internal and external signals that trigger tree dormancy release remain\nunclear (Singh et al. 2017; Yumoto, et al. 2024). In this study, we reveal the\ncontrolling effect of P_frq on autumn leaf senescence, a factor that has been largely\noverlooked ...\nSingh, R. K., et al. (2017). \"Photoperiod- and temperature-mediated control of\nphenology in trees – a molecular perspective.\" New Phytologist 213(2): 511-524.\nYumoto, G., et al. (2024). \"Seasonal switching of integrated leaf senescence\ncontrols in an evergreen perennial Arabidopsis.\" Nature Communications 15(1): 4719.\nResponse: Thank you very much and we have elaborated more in discussion according\nto your suggestions. Line 180-184, 506-511.\nOther minor comments\nL142 “G-F” should be “G-I”\nResponse: Thank you for pointing out this error and we have corrected it. Line 615.\nAnother minor comment is about the term “Scenario”. The three Scenarios by\ncontrolling different drivers in Figure 1 might confuse readers with the CIMPS SSP\nscenarios.\nConsidering change the scenario term in Figure 1 and corresponding text, for example\n“Scheme”, “Condition”, “Situation”, “Profile”, or other similar one that\nfits here best.\nResponse: Thank you for this suggestion and we have changed the term “scenario”\nto “scheme”.\nThank you for the updated version. My concerns have all been addressed. However,\nthere are a few minor comments and some clerical misspellings that need a further\ncheck by the authors.\nResponse: Thank you very much for your nice comments. We have carefully reviewed\nour manuscript and addressed the minor comments and corrected any clerical\nmisspellings.\nL142-143 “G–F shows … ”, should be “G-I show…” . I only see a dot line on\nthe plot, on dotted area. Please have a second check\nResponse: Thank you for pointing out these mistakes. To make it clear, we redrew\nthis figure and changed the significant regions with dot.\nL169 Figure 2 B and C: I suggest switching the x and y axes for these two plots,\nas you're showing how changes in Pfreq lead to changes in drought response times\n(lag and recovery). It would be clearer if you explained it like this: a 1-day\nincrease in Pfreq per month will, on average, result in a 14.3-month increase in\ndrought response lag (the inverse of the slope, 0.07) and a 6.7-month decrease in\nrecovery time. I understand there is considerable variation in these violin plots\ndepending on the timing of the drought and the plant's growth stage. Also, please\ndouble-check the unit of the slope in your plots B and C: are they in days per month?\nThe inverse value of 0.07 gives 14.3 months of increasing response lag with a 1-day\nincrease in precipitation frequency, probably unrealistic to me.\nResponse: Thanks for this nice suggestion and here are our explanations. The recovery\ntime and lag time calculations need multiple year observations and for each pixel\nover the time duration of 1982-2022, only one value can be determined. This value\nis then used to compare with the mean precipitation frequency for this pixel. And\nif we group all pixels together, we then get figure 2b,c. Therefore, the figure here\nis not the temporal change of precipitation on lag and recovery time, but the spatial\ndistributions of the correlations between the two variables. In addition, switching\nx and y axes can’t be considered possible to calculate the slope because the R2\nof the regression is not 1. Only when R2 of 1 could we have a reciprocal by switching\nx and y. In this case, we made it clear in the method section and captions that the\nfigure shows the spatial distribution of the two variables. To avoid confusion, we\nremoved the slope in the figures. We would like to make the x and y unchanged as\nin this way they can share the same y axis, which will give a better presentation\nfor figure illustrations.\nYou are using monthly Pfreq, so it's important to clearly describe the slope in terms\nof monthly days/year or a similar unit. Otherwise, the small trend values in\ndays/year per month could be mistaken for annual days/year. L37 “yarly” should\nbe “yearly”\nResponse: Thank you very much for your suggestions. We have made it clear in the\ncaption of Supplementary Figure 4. Additionally, we have corrected the typographical\nerror, changing “yarly” to “yearly”.\n3"}]

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