Latitudinal divergence in runoff responses to global forestation due to forest-atmosphere feedbacks

Domain: neuroscience
Published: 2026-02-09
Editorial outcome: major_revision

Abstract
Forestation is a pivotal nature-based solution for mitigating global warming, yet its unintended hydrological outcomes and associated geospatial patterns remain understudied. Here, we combine land-atmosphere coupled models with the Budyko framework to show that forest-atmosphere feedbacks dominate a latitudinal divergence in runoff responses induced by global potential forestation, with increases in tropical regions but declines in boreal regions. In tropical regions, substantial precipitation gains due to intensified upward moisture transport overwhelm the negative effects of forest-driven evapotranspiration (ET) enhancement. Conversely, in boreal regions, limited precipitation gains are insufficient to offset enhanced evaporative loss, driven by increased atmospheric demand due to elevated surface net radiation. The negative effects of direct forest expansion vary along the dryness gradient, with peak impacts in energy-water transitional regions. Our study highlights the necessity to incorporate hydrological considerations into carbon- or temperature-focused afforestation planning, and caution afforestation at high-latitudes where new forests may exacerbate water scarcity.

Introduction
Forestation—including afforestation, reforestation and forest management—has been advocated as a cost-effective nature-based strategy to curb climate change by sequestering atmospheric CO2 (refs. 1–5). To optimize the climate benefits of forestation, strategic tree planting in suitable locations is essential. Previous studies indicate that, when moving from tropical to boreal regions, not only does the carbon sequestration potential of forests decline, but the biophysical effects also shift from cooling to warming mainly due to a darkening of tree canopies, cautioning the limited climate benefits of afforestation in boreal regions 6–10. Such insights, however, rarely consider potential outcomes of forestation on the local hydrological cycle, particularly changes in freshwater availability through alteration to runoff.

Recently, an expanding body of literature has demonstrated that ecological restoration projects have caused a significant decline in land water availability due to increased water consumption through evapotranspiration (ET) by planted trees 11,12. The forest-driven exacerbation of freshwater availability poses a prominent threat to sustainable development of human society, particularly in dry regions where water resources are inherently scarce 13–15.

Forests, compared to short vegetation they replace, often exhibit higher ET rates because of their distinct functional traits and canopy boundary-layer characteristics. These include deeper roots that can access subsurface water reserves, denser canopy that enhances both transpiration and rainfall interception, lower surface albedo that increases available solar energy for ET, and lower aerodynamic resistance that facilitates stronger turbulent transfer 8,16–18. Previous assessments of the hydrological consequences of forest change have largely relied upon data from paired-watershed experiments (PWE) 19–22. This approach involved a comparison between two adjacent catchments that have similar characteristics over a prolonged period, except that one catchment has undergone major land cover changes while the other remains unchanged 23. Most PWE-based studies demonstrated that small-scale (i.e., catchment-level) afforestation leads to a decrease in surface runoff and soil moisture owing to the enhanced evaporative demand of growing trees 19,21,22,24,25. The runoff-suppressing effect of afforestation was also supported by analyses based on the Budyko framework, which also offers an empirical approach for attributing observed runoff changes into contributions from local atmospheric changes (i.e., altered precipitation and potential evapotranspiration, PET) and vegetation cover changes 26,27. A key underlying assumption of these studies is that atmospheric conditions, particularly precipitation, were independent of vegetation changes. However, more recent studies have reported opposing results for larger catchments, showing either minimal impact 28,29 or even increased surface water yields due to forest expansion 30,31. These apparent inconsistencies in the hydrological effects of forests may arise from potential rainfall feedbacks that become significant only for larger spatial scales of increased tree cover 32.

Recently, observational studies have increasingly recognized that vegetation-driven ET can promote downwind precipitation through atmospheric moisture transport and recycling 33–37. Although moisture-tracking models, which can effectively depict the teleconnections between moisture source (ET) and sink (precipitation), are capable of quantifying the impact of forestation on precipitation and subsequently runoff through recycling of evaporated moisture, this approach intrinsically overlooks the potential additional moisture imported from oceans due to reorganizations in atmospheric circulation patterns 38. Coupled Earth system models (ESMs) enable more comprehensive assessment of afforestation’s hydrological impacts by simultaneously accounting for direct recycling of evaporated moisture and alterations in atmospheric circulation patterns. Model-based studies suggest that forest changes, from regional to global scales, affect precipitation locally, and also remotely through synoptic-scale atmospheric anomalies (e.g., cyclone) or large-scale circulation shifts 39–44. Most existing modelling studies focus on the feedbacks of deforestation 41,43,45,46. However, given that the hydrological responses to afforestation and deforestation are asymmetric and that the regions suitable for potential afforestation are largely distinct from historically deforested areas 44,47,48, it remains essential to investigate the hydrological consequences of afforestation. In addition, these simulations predominantly rely on idealized (e.g., 100% or 50%) or historical afforestation/deforestation scenarios 44,49,50. Such scenarios fail to account for current and local-specific climate constraints on achievable forestation potential 6,44,50, thus potentially limiting their applicability in guiding science-based strategies for future afforestation initiatives.

Beyond altering precipitation, forestation also modifies near-surface meteorological conditions (e.g., surface net radiation, temperature, and humidity) 16, which influence atmospheric water demand by regulating PET, thereby affecting the partitioning of precipitation into runoff versus ET. Although the influence of PET changes is often considered secondary to that of precipitation, it can become more pronounced in energy-limited high-latitude regions 45. This is because the transition from snow-covered surfaces to darker forests substantially increases surface net radiation 10,51, which enhances PET and thus suppresses runoff generation. Conversely, in tropical regions where the atmosphere is more convectively unstable and oceanic moisture supply is abundant 52–54, high rates of moisture recycling over rainforests amplify precipitation responses to forest expansion 36,55. For example, previous modeling studies have suggested that large-scale afforestation enhances precipitation more substantially in the tropics than in boreal regions 44,49. These coexisting and counteracting mechanisms imply the balance between effects of precipitation and PET may vary geographically. So far, it remains unknown about the net effect of forest-atmosphere feedbacks on runoff, and the overall hydrological impact that further incorporates the direct forest-driven ET increase. These knowledge gaps motivate us to provide a more comprehensive quantification of runoff responses that account for the complex interplay between precipitation, PET, and ET, which can complement current afforestation policies that predominantly emphasize carbon sequestration or temperature modulation 7,8.

We here used a coupled land-atmosphere model—the Institute Pierre Simon Laplace climate model (IPSL-CM) 56,57—to characterise the potential hydrological outcomes of global forestation. To better inform the implications of global forestation strategies, as proposed to achieve “Net Zero” targets, we applied a full-potential forestation scenario using the Bastin et al.’s dataset (Bastin_Full), in which trees will be planted to the maximum potential of land carrying capacity 58 (Supplementary Fig. 1). Two equilibrium simulations were performed, prescribed identically with a fixed atmospheric CO2 concentration of 400 ppm (i.e., close to mean CO2 concentration over 2011–2020), and with the same boundary conditions of sea surface temperature (SST) and sea ice concentrations (SIC) at 400 ppm (Methods). The forestation experiment (SCE) is forced by the full-potential tree restoration map predicted by a machine-learning approach 58, and the control experiment (CTL) is forced by current land cover map 59. The difference between SCE and CTL determines all biophysical effects of global potential forestation on runoff. We then applied the Budyko framework 26,60,61 to further disaggregate runoff changes into the contribution from direct forest cover increase and forest-atmosphere feedbacks (i.e., changes in precipitation and PET).

Results
Hydrological effects of global potential forestation
We first quantified the hydrological footprint of global tree planting by comparing our two coupled simulations forced by full-potential and current tree cover maps (Fig. 1). At the global scale, full-potential forestation is estimated to increase terrestrial ET by 5.3% (Fig. 1e). The enhancement of ET is found over 70.3% of the land surface, and as expected, with the strongest increase occurring in regions with the highest afforestation potential (Fig. 1a and Supplementary Fig. 2a). The vegetation-driven ET increases are primarily attributed to enhanced canopy transpiration and interception, partially offset by reduced soil evaporation (Supplementary Fig. 3). Forestation-induced ET increase drives an intensification of the terrestrial water cycle by both enhancing the amount of terrestrial water vapour released to the atmosphere and also promoting turbulent and convective activities within the atmospheric boundary layer 62. Our simulations exhibit that precipitation generally increases in response to forest expansion (Fig. 1b and Supplementary Fig. 2b), which is approximately a 4.2% increase of global mean precipitation (Fig. 1e). However, compared to ET changes, the magnitudes and even signs of rainfall responses show stronger spatial variability, demonstrating that the recycling of evaporated moisture by rainfall is more complex than simply returning the moisture at the same location. Precipitation increases markedly in tropical and subtropical regions and in temperate wet regions, whereas in boreal regions it shows only slight increases or even decreases (Fig. 1b and Supplementary Fig. 2b). This spatial pattern is broadly consistent with the observed sensitivity of precipitation to vegetation 33,63.

The spatial mismatch between water source (ET) and water sink (precipitation) reshapes surface water availability. We find that precipitation changes are insufficient to compensate for the soil moisture loss induced by enhanced ET for most vegetated surfaces. Consequently, soil moisture depletion occurs across 66.9% of land area (Fig. 1c), causing a 2.7% decline in global mean soil moisture (Fig. 1e).

To understand the influence of global forestation on freshwater availability, we specifically focus on analyzing runoff responses (Fig. 1d). Our simulations show that, globally, full-potential forestation could lead to a significant increase in annual average runoff by 2.8%, suggesting that global forestation overall increases surface freshwater availability (Fig. 1e). This global value is relatively small due to the spatial compensation between regions with wetting (increased runoff) and drying (decreased runoff) trends. At the local scale, forestation could induce pronounced runoff changes (Supplementary Fig. 4a), with magnitudes ranging from 3.7% to 21.4% in most regions (corresponding to the 25th and 75th percentiles; Supplementary Fig. 4b). Notably, in some arid and semi-arid regions, such as Central Asia, the Sahel, South Africa, and Australia, although forestation causes small absolute runoff changes (<0.1 mm day-1), the relative changes can exceed 50% due to the low background runoff (Supplementary Fig. 4a).

Our analysis reveals that regional runoff responses depend tightly on the local climatic conditions, exhibiting greater spatial heterogeneity than ET and precipitation changes (Fig. 1d, f). In general, runoff exhibits divergent responses across latitudinal (or thermal) gradients, with increased runoff generally clustered in warm tropical and temperate regions, but decreased runoff mainly located in cold boreal areas. In most monsoon-controlled tropical or subtropical regions, including the Brazil, Sahel, the Congo Basin, southern Africa, southeastern China, Indonesia and northeastern Australia, precipitation increases frequently override concurrent increases in ET caused by extra forest cover and thereby result in increases in runoff (Fig. 1a, b, d). This runoff increase, driven by increase of precipitation exceeding that of ET, is also detected in some extremely cold regions, such as northeastern Siberia. In the Northern Hemisphere mid-to-high latitudes, particularly in Europe, Russia, and eastern North America, there is instead a reduction of runoff mainly because the increased precipitation cannot fully compensate for the enhanced ET (Fig. 1a, b, d). However, in northern Russia, the concurrent reduction in precipitation combined with the increased ET further amplifies the runoff decline (Fig. 1a, b, d). It is worth noting that the projected runoff decreased due to global forestation is also detected in some tropical regions, mainly clustered in South Asia and the Congo basin (Fig. 1a, b, d).

Since the idealised full-potential afforestation scenario (Bastin_Full) may overestimate the actual reforestation potential 64–66, we further incorporated additional constraints on the potential forestation maps to test the robustness of results. Building on Bastin_Full, a new scenario (Bastin_Refined_Climate) additionally excludes areas with frequent fire occurrences or with expected albedo-induced regional warming after afforestation, and another new scenario (Bastin_Refined_Climate&Land) further excludes croplands, built-up lands, and wetlands. Our simulations forced by refined potential forestation maps produced a latitudinal gradient of runoff responses similar to that of Bastin_Full, though with regional difference in the hydrological effects (Supplementary Fig. 5). Compared with Bastin_Full, reduced forestation area over tropical grasslands and savannas leads to less strong tropical runoff increase; and further exclusion of afforestation over croplands and urban areas in Bastin_Refined_Climate&Land show less significant decreases in high-latitude regions and weaker increases in low-latitude regions. (Supplementary Figs. 1 and 5). In both afforestation scenarios, the contrasting pattern of enhanced tropical runoff and suppressed high-latitude runoff remain evident.

To further verify our simulated latitudinal gradient of runoff responses to forestation, we calculated the observed sensitivity of precipitation minus ET (P–ET) to forest cover changes based on spatial regression, and retained grid cells where precipitation and ET both exhibit significant sensitivity to forest cover changes (Methods). Over multi-decadal timescales, P–ET serves as an approximate estimate for runoff. This observation-based analysis shows a clear latitudinal contrast: P–ET overall decreases with forest cover gain in high-latitude regions but increases in the tropics (Supplementary Fig. 6), in agreement with our model-based findings. Our results are also corroborated by previous model-based assessments of global forestation effects on the hydrological cycle. For example, De Hertog et al. 49. and Portmann et al. 44. consistently show a clear latitudinal gradient, with precipitation generally increasing in the tropics and decreasing or showing weaker changes at higher latitudes, in line with our findings.

Forest-atmosphere feedbacks driving the latitudinal contrast
Considering that ET is affected by both direct vegetation cover changes and indirect atmospheric changes (precipitation and near-surface meteorology as described by PET), we provide a quantitative assessment of the separate roles of land surface changes (ΔRland) and forest-atmosphere feedbacks (ΔRatm) in the forestation-induced runoff changes, based on the Budyko framework 45,61 (Methods). This framework characterizes land surface controls on runoff using a region-specific parameter n, which represents the ability of a watershed or region to retain precipitated water for ET. Higher values of n indicate a greater partitioning of available precipitation toward ET rather than runoff. The impacts of altered surface parameter n on runoff (ΔRland) refer to the runoff changes caused by terrestrial ET alterations under conditions of unchanged precipitation and PET. Forest-atmosphere feedbacks on runoff are further decomposed into contributions from precipitation (ΔRP) and PET (ΔRPET) changes. Here, we mainly employed two commonly used variants of the Penman equation for PET estimation: the open-water Penman equation (Penman-OW) 67 and the reference crop Penman-Monteith equation (PM-RC) 68 (Methods).

Our key finding is that the divergent runoff responses to forestation between tropical and boreal regions, as shown in Fig. 1d, f, are driven by forest-atmosphere feedbacks (ΔRatm; Fig. 2a, b), rather than by direct land cover changes (ΔRland). Compared to ΔRland that show globally consistently negative effects and particularly in mid-to-low latitudes (Fig. 2c, d), the dominant forest-atmosphere feedbacks produce opposite responses along latitudinal gradients: widespread runoff reductions in northern high latitudes (e.g., Eurasia), whereas runoff enhancements in mid-to-low latitudes (e.g., the Brazil, Africa, Southeast Asia and southern Europe) (Fig. 2a, b). Furthermore, the two further constrained forestation scenarios consistently show that forest-atmosphere feedbacks strongly shape the latitudinal gradient of runoff responses (Supplementary Fig. 7).

While the Budyko-based attribution highlights the dominant role of forest-atmosphere feedbacks in driving runoff responses, it does not explicitly account for the influence of altered precipitation characteristics, such as rainfall intensity and snow fraction, on runoff generation processes 69. To address this potential bias, we adopted a modified approach following Zhou et al. 61., which isolates the effects of altered precipitation characteristics and incorporates these into atmospheric feedbacks (Methods). The results from this modified method align well with our main findings, confirming that forest-atmosphere feedbacks predominantly drive the latitudinal divergence of runoff responses (Fig. 3 and Supplementary Fig. 12). As forestation-induced precipitation changes shift from strong increases in tropics to modest increases or even slight declines at high latitudes (Fig. 1b), the resultant ΔRP exhibits a similar spatial pattern of precipitation responses (Fig. 3a and Supplementary Fig. 12a). Compared to precipitation, PET demonstrates opposite signs of effects on runoff across latitudinal gradients: PET decreases drive modest runoff increases at mid-to-low latitudes, whereas PET increases drive strong runoff reductions in high latitudes (Fig. 3b, Supplementary Figs. 12b and 13a, c). In warm regions, precipitation changes lead to a strong runoff increase (+6.4%), which is slightly amplified by a modest PET-driven runoff rise (+0.2%) (Fig. 3c). The precipitation-driven runoff increments are most prominent in some monsoon regions, with values exceeding 40% (Supplementary Fig. 4e). Conversely, in cold regions, the PET-driven runoff reduction (−2.0%) becomes non-negligible, largely offsetting the precipitation-driven increase (+3.3%) (Fig. 3c). At some locations, forestation-driven PET changes could reduce runoff by over 20% (Supplementary Fig. 4f).

We next explore the physical mechanisms underlying the spatial variability of precipitation and PET responses to forestation. By aggregating the grid-level changes to regional scale, we find that precipitation significantly increases in both warm and cold regions (Fig. 3d). However, the magnitude of precipitation increment in cold regions is only about one-third of that in warm regions (Fig. 3d). We further decompose precipitation change into the contribution from four physical processes alterations: thermodynamic effects associated with atmospheric water vapour (ΔWV), vertical moisture transport (ΔVert), horizontal moisture advection (ΔAdv), and terrestrial ET (ΔET) 70–73. The residual term (Res) representing differences between modelled precipitation changes and the sum of all processes. Our analysis shows that, local ET enhancements contribute comparably to precipitation enhancement in both warm and cold regions, and thus cannot explain the asymmetric precipitation enhancement in warm versus cold regions (Fig. 3d). Instead, atmospheric dynamics and thermodynamics are responsible for the latitudinally asymmetric precipitation changes (Fig. 3d and Supplementary Fig. 14).

In warm regions, forestation-induced precipitation increases are primarily contributed by ΔVert (Fig. 3d and Supplementary Fig. 14c). This is because forestation significantly enhances the Convective Available Potential Energy (CAPE) and the vertical gradient of specific humidity (Supplementary Fig. 15a, b), indicative of reduced atmospheric stability, thereby promoting the upward motion of moisture (Supplementary Fig. 15c) and increasing convective precipitation (Supplementary Fig. 14c). The changes in vertical velocity throughout the troposphere are intrinsically linked to forestation-induced alterations in atmospheric circulation and moisture convergence (Supplementary Fig. 15c). This precipitation increase is further amplified by ΔAdv (Fig. 3d), due to enhanced horizontal advection of moisture from upwind regions (Supplementary Fig. 15c). For example, the southeasterly winds over Southeast China are strengthened, enhancing moisture transport from the ocean and consequently favoring advective rainfall in that region (Supplementary Figs. 14b and 15c). The dominance of dynamic processes (ΔVert + ΔAdv) is particularly evident in monsoon-affected regions, including South America, Africa, East Asia (Supplementary Fig. 14b, c). This suggests that large-scale sea breeze circulations, as perturbed by global forestation, tend to converge more ocean-sourced moisture, thereby enhancing regional precipitation recycling. In addition, the effects of thermodynamic processes, ΔWV, are also non-negligible in warm regions (Fig. 3d and Supplementary Fig. 14a). The residual components also exhibit a precipitation reduction in warm regions, particularly in southeastern China (Fig. 3d and Supplementary Fig. 14c), which may be related to weakened transient eddies (storm systems) 74.

In cold regions, while global forestation also causes reorganizations of atmospheric circulation patterns (Supplementary Fig. 15), the circulation-induced precipitation changes are insignificant regionally because the positive and negative effects spatially offset each other (Fig. 3d and Supplementary Fig. 14b, c). However, at local scales, the circulation-induced impacts on precipitation remain substantial. For example, over Eurasia, the marked reduction in CAPE suppresses upward air motion and consequently weakens convective precipitation (Supplementary Figs. 14c and 15). In addition, the large anticyclonic anomaly induced by forestation over Eurasia weakens the prevailing westerlies, thereby reducing moisture advection from the North Atlantic and inhibiting advective precipitation. (Supplementary Figs. 14b and 15c). Beyond such effects of dynamic processes, thermodynamic processes associated with changes in atmospheric water vapor (ΔWV) further contribute to an insignificant precipitation reduction in cold regions, particularly over Europe and western North America (Fig. 3d and Supplementary Fig. 14a).

Apart from precipitation changes, forestation also triggers asymmetric responses in PET, with warm regions showing a 0.7% (± 0.6%) decrease and cold regions a 1.0% (± 2.0%) increase (Fig. 3e and Supplementary Fig. 13a, c). These contrast PET responses arise primarily from the trade-off between alterations in surface available energy (Rn−G, representing the surface energy constraint) and atmospheric aridity (indicated by vapor pressure deficit, VPD) (Fig. 3e and Supplementary Fig. 16). Forestation tend to increase Rn−G by lowering surface albedo, but this is insufficient to explain the latitudinal asymmetric PET responses because of the comparable magnitudes of radiation gains between warm and cold regions (warm: +4.0%; cold: +3.7%). Furthermore, forestation tend to reduce VPD, with a greater reduction detected in warm regions (−5.0%) compared to cold regions (−1.9%), suggesting VPD as the primary driver of asymmetric PET responses. By disentangling temperature and humidity contributions to VPD changes (Supplementary Text 1), we demonstrate that forestation lead to larger VPD reductions in warm regions due to stronger cooling and greater humidity increases, whereas in cold regions, the warming induced by forestation partially counteracts the humidity-driven decline in VPD, resulting in a smaller net decrease (Supplementary Fig. 17). Consequently, the negative effects of reduced VPD are sufficient to counterbalance the positive effects of increased net radiation in warm regions, but not in cold regions, resulting in a PET decrease in warm regions but a PET increase in cold regions (Fig. 3e).

Aridity-level regulation on direct land surface effects
Compared to the circulation-mediated atmospheric feedbacks, direct effects of forest cover increase on runoff (i.e., ΔRland) are more relevant to freshwater availability at the location of tree planting (Fig. 2c, d). Our results reveal that ΔRland could partially offset the ΔRatm-driven runoff increase in tropical regions (e.g., Africa and South America), whereas completely neutralize or even reverse the ΔRatm-driven increase in some temperate zones (e.g., Europe), thereby amplifying the latitudinal divergence in runoff responses (Figs. 1d, f and 2). This more universal negative effect of ΔRland in mid-to-low latitudes are captured by the forestation-induced increase in the surface parameter n (Supplementary Fig. 13b, d), which represents catchment characteristics governing runoff generation and primarily reflects the catchmental water retention capacity 26,60,75–77. The consistent increase in n indicates that afforestation-induced catchmental changes overall enhances rainfall portioning into regional ET, thereby reducing runoff. Along the dryness gradient, the ΔRland-driven runoff decrease exhibits a unimodal pattern, with the most pronounced decrease observed in transitional regions where the P/PET value approaches unity (Fig. 2c, d). In those afforestation hotspots, including Europe, southeastern North America, southern Asia and Africa, the adverse effect of ΔRland could reduce regional runoff by over 40%, far exceeding the global average decline of just 3.7% (Fig. 2c and Supplementary Fig. 4d). These findings are consistent with theories that ET is constrained by water limitations in dry regions and by energy limitations in cold regions 78,79.

To better illustrate how background aridity levels affect runoff responses to tree planting, we fitted the Budyko curves — the relationships between the evaporative index (ET/P) and the dryness index (PET/P)—using the global 60-year mean data from both the CTL and SCE scenarios (Fig. 4 and Supplementary Fig. 18, Methods). The shape of curves reflects the global-scale surface characteristic parameter (n). Our results show that potential forestation increases the parameter n from 3.6 in the CTL to 4.1 in the SCE. This indicates a greater partitioning of precipitation towards ET (increased ET/P) driven by enhanced ecosystem water demand from forests, leading to less runoff (decreased R/P, the ratio of runoff to precipitation). Without accounting for precipitation and PET changes, decreased R/P would directly result in runoff yield reduction. The Budyko curves in Fig. 4 illustrate unimodal dependence of ET/P (1-R/P) on the background dryness, peaking at around PET/P ≈ 1 that represent transitional zones between energy-limited and water-limited regions. The effect of tree planting on the PET/P–ET/P relationship is also robust to the selection of different percentiles used for the fitting of the global Budyko curve (Supplementary Fig. 19).

Methods
Equilibrium experiments with coupled land-atmosphere models
In this study, we used the IPSL-CM coupled land-atmosphere climate model to simulate the biophysical effect of global full-potential forestation on runoff under equilibrium climate conditions. IPSL-CM was developed by the Institute Pierre Simon Laplace modeling community with a resolution of 1.26° × 2.5° and has participated in the Coupled Model Intercomparison Project (CMIP) phases 5 and 6 (refs. 56,57). It consists of two major components that have been updated to the latest version: the Laboratoire de Météorologie Dynamique atmospheric general circulation model with zooming capability (LMDZ, version 5, v2076) and the Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE, v3035) land surface model. LMDZ, featuring 19 vertical layers, characterizes dynamics of atmospheric physical processes such as convection, boundary layer, and cloud 103,104, and ORCHIDEE describes water and energy balance, vegetation, and soil carbon cycle in terrestrial ecosystems 105. The ORCHIDEE model divides terrestrial plant function types (PFTs) into 13 categories, including 12 vegetation types and one bare ground type, each with independent parameters to calculate vegetation physiological processes. The IPSL-CM has been widely used to quantify the biophysical impacts of vegetation changes on temperature and the hydrological cycle 40,106,107, for its outstanding performance in simulating key vegetation-related processes, including the ratio of vegetation-driven transpiration to the evapotranspiration, T/ET 108. Model performance was further evaluated using observed streamflow records of 270 screened catchments from the Global Streamflow Indices and Metadata Archive (GSIM) 109,110 (Supplementary Text 3). The IPSL-CM successfully reproduces cross-catchment runoff variability (r=0.66, p<0.01), with the median of model-observation difference statistically indistinguishable from zero (Supplementary Fig. 26). This indicates no systematic bias in the IPSL-modelled runoff, supporting the model capability for assessing afforestation-induced runoff responses.

The biophysical impacts of forestation on the hydrological cycle were isolated by a pair of equilibrium experiments. The two experiments differed solely in their prescribed PFT conditions: the “CTL” run was forced with current PFT maps, whereas the “SCE” run was prescribed with the hypothesized PFT maps with global forestation to the full of potential of terrestrial ecosystems. The experiment pair was conducted under the atmospheric CO2 concentration of 400 ppm. Each equilibrium experiment was run for 100 years. We used the last 60 years of monthly output data for further analysis because the simulations typically reached an equilibrium state in around 40 years, and selecting 60 years also helps reduce the potential influence of inter-decadal and multi-decadal climate variability. Each of the last 60 years could be regarded as an independent run under the equilibrium state, allowing us to estimate the uncertainty in forestation-induced biophysical response.

The current land cover map was derived from the Olson categories, containing 94 land cover types, which were developed based on more than 20 years of field investigations since 1980 59. The conversion from Olson to PFT categories is done within the code of ORCHIDEE based on cross-walking table. The PFT map based on Olson categories was provided by ORCHIDEE guidelines (https://forge.ipsl.jussieu.fr/orchidee/wiki/Documentation/Ancillary). The potential tree cover across the globe was derived from Bastin et al. 58. Bastin et al. developed a random forest machine-learning model to extrapolate tree cover measurements from protected areas to the globe using satellite-observed environmental conditions. Notably, the tree cover potential has been heavily debated, as it may be overestimated 64–66. Therefore, this forestation potential should be regarded as an upper limit for global current tree cover. In the forestation scenario (SCE), the forest increase from current to potential levels was assigned to different forest types using their observed present-day fraction (used in the CTL run) as the weighting factor. This allocation principle ensured that dominant forest type increased most substantially in the SCE run. In addition, different definition and classification methods of forest among data sources or overfit of the machine learning models might lead to less forest cover of the potential level than the current level in a small fraction of vegetated lands. In such cases, we assigned the forest cover in the SCE run as that in the CTL run, suggesting no afforestation attempts.

The coupled land-atmosphere model does not incorporate the ocean module, which help limit potential biases propagated from oceanic processes. To ensure accurate representation of conditions over the oceans, the model experiments were constrained by employing multi-model averaged sea surface temperatures (SSTs) and sea ice concentrations (SICs) from the CMIP6 1pctCO2 simulations at 400 ppm. There are 41 ESMs providing the available outputs, including ACCESS-CM2, ACCESS-ESM1-5, CAMS-CSM1-0, CAS-ESM2-0, CESM2, CESM2-FV2, CESM2-WACCM, CESM2-WACCM-FV2, CIESM, CMCC-CM2-SR5, CanESM5, CanESM5-CanOE, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, E3SM-1-0, EC-Earth3, EC-Earth3-AerChem, EC-Earth3-Veg, GOALS-f3-L, FGOALS-g3, FIO-ESM-2-0, GFDL-CM4, GFDL-ESM4, GISS-E2-1-H, HadGEM3-GC31-LL, HadGEM3-GC31-MM, INM-CM4-8, INM-CM5-0, IPSL-CM6A-LR, KIOST-ESM, MIROC-ES2L, MIROC6, MPI-ESM-1-2-HAM, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NESM3, NorCPM1, NorESM2-MM, UKESM1-0-LL.

We also conducted numerical experiments using another coupled land–atmosphere model, CESM version 2.0, to verify the robustness of our results. This model was developed by the National Center for Atmospheric Research (NCAR) with a spatial resolution of 1.9° × 2.5° (ref. 111.). It employs the Community Atmosphere Model version 5 (CAM5) as its atmospheric component 112 and the Community Land Model version 4.0 (CLM4) as the land surface component 113. Similarly, we set the CO2 concentration at 400 ppm and used multi-model averaged SSTs and SICs under this CO2 concentration as boundary conditions. We conducted two 100-year equilibrium simulations with different PFT inputs: CTL (control) with current PFTs using Olson land cover map 59, and SCE (forestation scenario) with full-potential forestation PFTs based on Bastin et al.’s global potential tree cover map 58. Considering the required model spin-up time, we analyzed data from the last 60 simulation years. It is worth noting that while CESM-simulated runoff is significantly correlated with observations, CESM overall underperforms IPSL (Supplementary Fig. 26). We therefore present the CESM-based results in the Supplementary Information to support the robustness of our primary findings from IPSL.

Two sensitivity experiments with constrained forestation
As previously noted, the Bastin et al.’s data likely overestimate potential tree cover; thus, our full-potential forestation scenario (Bastin_Full) represents the upper bound of global forestation potential. In this regard, we designed two additional scenarios (Bastin_Refined_Climate and Bastin_Refined_Climate&Land) by progressively introducing more realistic constraints to test the robustness of our results. These three scenarios represent a range of possible forestation pathways, from the theoretical maximum to the most practical feasible.

Bastin_Refined_Climate builds upon Bastin_Full by excluding regions with frequent fires and where forestation would produce net warming due to reduced surface albedo. This refinement addresses two major critiques of the Bastin et al.’s dataset: overestimated potential in fire-prone grassland and savanna ecosystems, and the neglect of forestation-caused regional warming due to reduced albedo 65,66. Following Fesenmyer et al 64., we used the 500m MODIS Burned Area dataset to remove all non-cropland areas experiencing two or more fires during 2002–2022, assuming that such frequencies substantially limit tree density 114. We further excluded regions where albedo-induced radiative warming exceeds the cooling from carbon sequestration of potential forestation, based on the dataset of Hasler et al. 115. These constraints markedly reduce the estimated afforestation potential in tropical grassland and savanna ecosystems, particularly across Africa (Supplementary Fig. 1b).

Bastin_Refined_Climate&Land incorporated additional constraints on Bastin_Refined_Climate by excluding croplands, built-up areas, and wetlands to ensure land-use sustainability and feasibility. Urban and agricultural areas, given their essential roles in food production and human livelihoods, were masked out using the ESACCI Global Land Cover dataset for 2000. Wetlands were excluded using the Global Lakes and Wetlands Database (GLWD, version 2) to prevent potential losses of large soil organic carbon (SOC) stocks following afforestation. Compared with the Bastin_Refined_Climate scenario, the further reduction in forestation potential in Bastin_Refined_Climate&Land predominantly occurs in cropland-dominated regions like Europe, African croplands, Southeast Asia, and eastern South America (Supplementary Fig. 1c).

Observed sensitivity of P–ET to forest cover change
To quantify the observation-based sensitivity of P–ET to forest cover change, we used precipitation data from the Multi-Source Weighted-Ensemble Precipitation (MSWEP; 1° × 1° resolution), ET data from the Global Land Evaporation Amsterdam Model (GLEAM; 0.1° × 0.1°), forest cover data from the European Space Agency Climate Change Initiative (ESACCI; 30m), near-surface air temperature from the Climatic Research Unit Time-Series (CRUTS; 0.5° × 0.5°), and incoming shortwave radiation from the Clouds and the Earth’s Radiant Energy System (CERES; 1° × 1°). Considering the role of mesoscale and large-scale circulation in redistributing atmospheric moisture, which can obscure the forest-precipitation signal, we analyzed 20-year means (2001–2020) rather than individual years. All datasets were resampled to a common 1° × 1° grid to match the coarser spatial resolution of model outputs.

For each vegetated grid cell, we performed regressions of precipitation (or ET) against forest cover, air temperature, and incoming shortwave radiation within a 3 × 3 window centred on the pixel, to estimate the sensitivity of precipitation (or ET) to forest cover change. The difference between precipitation and ET sensitivities represents the sensitivity of P–ET to forest cover changes, which approximates runoff responses over long-term scales. It is important to note that most observation-based space-for-time substitution approaches assume that forested and non-forested landscapes share similar atmospheric backgrounds, and thus cannot capture the influence of forest change on atmospheric circulation that redistributes moisture and affects precipitation. We hypothesize that this moisture redistribution effects of circulation diminishes the local forest–precipitation signal. Hence, we only selected pixels where precipitation and ET sensitivities to forest change were both statistically significant (p<0.05), despite their limited number.

PET
In the climate models, PET is not directly available but can be calculated using model outputs of meteorological variables. To avoid the dependence of our results on the formulization of PET, we adopted two widely used versions of the Penman-Monteith model, including Penman-OW and PM-RC model. The Penman-OW model is a simplified version of the Penman-Monteith model, which is applied to open-water surface 67:

PET = (s Rn - G + 6.43(1 + 0.536u)γD) / (λ(s + γ)) (1)

The PM-RC model is another standardized version that is applicable to an idealized reference crop surface 68:

PET = (0.408s(Rn - G) + γ(900 / (T + 273)) uD) / (s + γ(1 + 0.34u)) (2)

In these equations, s is the gradient of the saturation vapour pressure with respect to temperature (KPa K−1), γ is the psychometric constant (0.0665 kPa K−1), λ is the latent heat of vaporization (2.45 KJ kg−1), T is the air temperature at 2m above ground (°C), u is the wind speed at 2m height (ms−1), D is the vapour pressure deficit (VPD) of the air at 2m height as the difference between the saturation vapour pressure (es) and actual vapour pressure (ea) (kPa), Rn - G is the available energy (MJ m−2 d−1). The quantities s and D are computed according to

s = 4098es / (T + 273.3)2 (3)

D = es - ea (4)

es = 0.6108 exp(17.27T / (T + 273.3)) (5)

ea = P × SH / (0.378SH + 0.622) (6)

where es is saturation vapour pressure (kPa) with respect to T, ea and SH are the actual vapour pressure (kPa) and specific humidity (kg kg-1) at 2m height, respectively. P is the surface air pressure.

However, the Penman equation may overestimate PET because the underlying assumption of a saturated land surface is typically not satisfied. To address this issue, we also calculated PET using an energy-based approach proposed by Zhou and Yu 116,117 (referred to as PETe). PETe represents the maximum ET constrained by the energy supply (Rn) under conditions of saturated surface. PETe is calculated using the net radiation (Rn) and the wet Bowen ratio (βw), given by:

PETe = (Rn / λ) * (1 / (1 + βw)) (7)

βw is estimated based on the aerodynamics, that is, the ratio of sensible over latent heat exchanged between the saturated surface and the atmosphere, given by:

βw = γ * (Ts - Ta) / (es* - ea) (8)

Where Ts - Ta is the difference between surface (Ts) and air (Ta) temperatures, and es* - ea is the difference in vapor pressure at the saturated surface (es*) and of the air (ea) above.

We noted for some grid cells, the PM-based and energy-based PET was smaller than the model-output ET, especially in energy-limited high latitudes. The potentially underestimated PET in such locations may lead to inaccurate n values as represented in the Budyko model. To address this issue, we replaced PET values with the ET values if the empirically calculated PET is less than ET.

Untangling the land surface effect and atmospheric effect with Budyko framework
The Budyko model assumes that precipitation partitioning at the surface is determined by climatic conditions and land surface characteristics under steady-state climatic conditions of the climatological (at least 30-year) scale water balance (i.e., P = ET + R) 118. It should be noted that our equilibrium experiments meet this steady-state condition assumption. Within the Budyko framework, Climatic conditions refer to the precipitation and near-surface meteorological condition (i.e., PET), while land surface characteristics, such as vegetation, soil, land use and topographic factors, are encapsulated with a region-characteristic parameter n in Budyko models 78,79. Here, we adopted the Choudhury–Yang equation 79, a widely used Budyko model, to decompose the change in runoff caused by tree planting into contributions from the atmospheric changes and land surface changes. In this Budyko model, the fraction of evapotranspiration to precipitation (ET/P) is expressed as a function of the dryness index (PET/P) and the region-characteristic parameter (n):

ET / P = (1 + (PET / P)n) ^ (-1/n) (9)

Based on the global 60-year data of P, ET and PET, we first estimated the parameter n in each grid-cell by solving the above equation. Then we decomposed the changes in runoff (ΔR) as the sum of contributions from changes in P, PET and n by combining the first-order Taylor expansion of Choudhury-Yang equation and the water balance assumption:

ΔR = ΔP - ΔET = (1 - ∂ET / ∂P) ΔP - (∂ET / ∂PET) ΔPET - (∂ET / ∂n) Δn (10)

where the three partial derivatives can be analytically derived from the Eq. (9), given by:

∂ET / ∂P = ET / P * (PETn / (Pn + PETn)) (11)

∂ET / ∂PET = ET / PET * (Pn / (Pn + PETn)) (12)

∂ET / ∂n = ET / n * (ln(Pn + PETn) - (Pn ln P + PETn ln PET) / (Pn + PETn)) (13)

In practice, calculating the above sensitivity coefficients using only 60-year data of either CTL or SCE may introduce some bias in the contributing components. Zhou et al 61. suggested that using the mean values of the sensitivity coefficients calculated by CTL and SCE data can best estimate the contributions of P, PET and n, given by:

ΔRP = 1/2 * (∂ET/∂P1 + ∂ET/∂P2) * ΔP (14)

ΔRPET = 1/2 * (∂ET/∂PET1 + ∂ET/∂PET2) * ΔPET (15)

ΔRland = 1/2 * (∂ET/∂n1 + ∂ET/∂n2) * Δn (16)

where the subscript 1 and 2 denote the 60-year period in CTL and SCE, respectively.

Then we can obtain the effect of forest-atmosphere feedbacks (ΔRatm = ΔRP + ΔRPET) and land surface changes (ΔRland) on the estimated forestation-induced runoff changes.

Sensitivity tests of Budyko attribution
We first evaluated the robustness of our Budyko-based attribution analysis to different methods of estimating PET. The decomposition of runoff changes was repeated using outputs from the final 60 years of the equilibrium simulations with two Penman-type PET equations (Penman-OW and PM-RC) and an energy-balance-based PET (PETe). All three approaches yielded consistent attribution results, supporting our main conclusion that forest-atmosphere feedbacks dominate the latitudinally contrasting runoff responses to global forestation (Supplementary Fig. 10). This consistency is present despite the tendency of greater Penman-based PET than PETe particularly in arid regions (Supplementary Fig. 27), as also noted by Zhou and Yu 116,117. This PET difference has minor influence on our results (Supplementary Fig. 10) likely because potential forest expansion is mainly located in humid regions. Moreover, the Penman equations explicitly represent afforestation-induced increases in near-surface humidity and its suppression on atmospheric moisture demand—an effect that the energy-balance approach cannot fully capture (Supplementary Figs. 13a, c, e and 16). As the humidity-related effects are an important component of the forest-atmosphere feedbacks, our main results based on Penman-derived PET, while the results using PETe are presented in the Supplementary Information.

In addition to the use of 60-year equilibrium simulations, we further examined how climate variability across different timescales might affect the attribution of runoff responses. To this end, we implemented a bootstrap resampling approach to generate 1000 random year samples (with replacement from the final 60 years), for 30-, 40-, 50-, and 60-year timescales, respectively. For each sample, the forestation-induced runoff change was calculated as the difference between the SCE and CTL simulations and further decomposed into atmospheric feedback and land-surface components using the Budyko framework. The mean of the 1000 realizations represents the average runoff response for each timescale. The ensemble means derived from the 30-, 40-, 50-, and 60-year subsets produced results consistent with our main findings (Supplementary Fig. 9), indicating that internal climate variability has minimal influence on our conclusions.

Finally, we assessed the potential influence of forestation-induced changes in precipitation characteristics (e.g., rainfall intensity, snowfall fraction) on the Budyko-based attribution of runoff changes. In cold regions, for example, a considerable portion of precipitation falls as snow during winter, and the subsequent spring or early-summer snowmelt substantially contributes to seasonal runoff. Since the standard Budyko framework only accounts for the first-order control of mean climate (precipitation and PET) and land surface conditions, the effects of precipitation characteristics on runoff (e.g., through the inter-seasonal processes) were implicitly conflated into the data-approximated n (ref. 69.). Hence, this may bias our interpretation of n changes as resulting from altered land surface properties. To address this bias, we adopted a modified Budyko-based approach for attribution following Zhou et al 61., which isolates the portion of precipitation change solely driven by altered precipitation characteristics (P*). In this approach, the P*-related portion of precipitation is artificially partitioned into runoff and directly subtracted from total precipitation and runoff, to present its effect being conflated into catchment properties (n) (Eq. 17). This method does not consider the potential effect of forestation-induced changes in land surface properties on runoff generation, implicitly assuming that runoff generation depends only on precipitation characteristics. Therefore, the ratio of the remaining runoff (R2 - P*) to the remaining precipitation (P2 - P*) in the forestation experiment is then assumed to be identical to that in the control simulation:

(R2 - P*) / (P2 - P*) = R1 / P1 (17)

where subscripts 1 and 2 denote the CTL and SCE simulations, respectively, and subscript 2' suggests that the effect of precipitation characteristics (P*) has been excluded for the forestation scenario. Solving for P* yields:

P* = (R2 * P1 - R1 * P2) / (P1 - R1) (18)

Further, the effects of changes in the remaining precipitation (P2'), PET, and n on the remaining runoff (R2') are decomposed under the Budyko framework. This allowed us to identify the more realistic climate change effect (changes in the mean P, PET and precipitation characteristics) and land surface effect on forestation-induced runoff changes:

ΔRatm = (∂ET/∂P * ΔP + ∂ET/∂PET * ΔPET) + P* (19)

ΔRland = (∂ET/∂n * Δn) (20)

Together with the standard Budyko-based attribution, this extended approach, which explicitly accounts for precipitation characteristics, provides a refined estimate for the contribution of atmospheric feedbacks and land surface changes. Results from this sensitivity test remain consistent with our main findings, confirming that forest-atmosphere feedbacks predominantly drive the latitudinal contrast in runoff responses to global afforestation.

Moisture budget decomposition
To understand the mechanisms driving precipitation change, precipitation is decomposed into four components based on the vertical integration of the moisture budget 71:

P' = - ∫ (V' ∇q + ω' ∂q/∂p) dp - ∫ (V ∇q' + ω ∂q'/∂p) dp + E' + Res (21)

Here the overbar operator ¯ denotes climatology in CTL and ' represent the changes caused by global forestation (SCE-CTL). P, ω, q, V, E, Ps are precipitation, vertical pressure velocity, specific humidity, horizontal velocity, ET and surface pressure, respectively. The four main terms on the right hand side represent the thermodynamic effect associated with water vapour changes (ΔWV), altered horizontal moisture advection (ΔAdv) associated with horizontal wind changes, altered vertical dynamic effect (ΔVert) associated with vertical velocity changes, land surface ET changes (ΔET). The residual term (Res) is the precipitation change that is unexplained by above four processes, representing the impacts of transient eddies.

Global Budyko curve and global-scale surface parameter
We fitted the relationship between the evaporative index (ET/P) and the dryness index (PET/P) using global 60-year mean data (Supplementary Fig. 18). PET is the mean value of the two PET algorithms (Eqs. (1) and (2)). We binned the ET/P values according to their corresponding PET/P values. Following Ukkola et al. 119., we determined the 5th and 95th percentile values of ET/P for each 0.1-wide PET/P bin. Then we fitted the Budyko curve and estimated the global-scale n value based on the global data with ET/P values falling between the 5th and 95th percentiles. The 5% and 95% thresholds were chosen to ensure that as much data as possible was used and to exclude outliers that could introduce bias into the fitted parameters. Apart from the fitted Budyko curve, we also plotted boxplots with 0.2-wide PET/P bin in Fig. 2 to better demonstrate the impacts of global forestation on ET/P–PET/P relationship.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study, Kan et al. conduct a fairly comprehensive Earth system modelling (ESM)-based experiment to assess the impact of global forestation on runoff. For this, in addition to a control simulation, they run the ESMs with the tree restoration potential map from Bastin et al. 2019. They combine the bottoms-up modelling with a simple top-down Budyko framework to partition the drivers of runoff changes among atmospheric and land processes. While the paper is well-written and the experiment is logically designed, I have some reservations about the use of the Bastin et al. dataset and the over-interpretation of the Budyko framework. As a result, I suggest major revisions before the manuscript can be considered for publication in Nature Communication. I detail my concerns below.\nMajor Comments\n1. Although I understand to some extent the logic behind using the Bastin et al. 2019 data, I am not sure if it represents the “maximum potential” as the authors describe. Most criticisms of the dataset make it clear that the tree restoration potential has been vastly overestimated. Hence, this cannot be considered as the potential. I request the authors to motivate the use of this dataset better. One possible option is to do the analysis for various degrees of restoration potentials but I am not sure how computationally feasible this option is. If not, this represents a weakness in the experiment design and the results are not exactly a realistic scenario, which the authors are aiming for.\n2. Another source of uncertainty and one that is fully acknowledged in the study is the issue of fixed rooting depths prescribed in the ESMs. Even if the rooting depths show inter-pft variability, there is large variance intra-pft and this would cause a lot of spatial variability in soil water uptake. I acknowledge that this is not something that can be easily addressed but should, ideally be discussed. For example, In line 337, the authors say that ET is enhanced but with shallower root depths, the vegetation might not have the capacity to access potentially depleted soil moisture.\n3. This is not exactly a suggestion but more of an observation: it is interesting that the runoff increases in the tropics and decreases in the boreal regions. As the boreal regions are fairly humid, it would be interesting to see if they go past a key aridity threshold (humid to dry-subhumid for example).\n4. Much of the results and the consequent conclusions depend on how good the hydrological model within ORCHIDEE is? Considering that LSMs are not intended for hydrological modelling, the authors should provide more details on how the runoff from the model is validated, and to what degree can they be trusted? In this regard the manuscript is very light on the details.\n5. One of the biggest advantages of the study is the fully coupled model which simulates the remote effects of local forestation, and which is very nicely shown in the study. However, the authors predominantly focus on moisture recycling. How about the impact of forestation on albedo changes and potential subsequent changes in the atmospheric circulation? Does the increased atmospheric moisture affect the stability of the atmosphere, thus affecting precipitation? In this regard, I find the discussion of the results very sparse and superficial.\n6.Calling the Budyko parameter as “water retention capacity” is not exactly correct. The partitioning of precipitation into evaporation is not only because more water is stored in the watershed but also due to lateral flow out of the watershed. The parameter is more an integral of all the factors that could not be explained by the original non-parametric Budyko hypothesis. Maybe a better phrase is “partitioning factor”?\n7. In line with the previous comment, I feel that the utility of the Budyko hypothesis has been stretched in the study. First, it was not originally intended to be used at grid-cell level but more at large catchment-scale, and at very long-term timescales (greater than 20–30 years). While a lot of studies have over-used the framework, I am not exactly sure how to suggest improvements here but at the very least, these limitations have to be acknowledged. For example, line 215: The applicability of the hypothesis in cold regions is questionable.\n8. Line 163: 80% seems a bit misleading in arid or semi-arid ecosystems considering the . Maybe also mentioning the absolute values is a bit more transparent.\n9. Line 287: The average numbers are quite small (near-zero). Please show the standard deviation (or the spread).\n10. Line 348 – 357: Seems like this is a summary of the results but not discussion.\nMinor Comments\nLine 29: ‘Direct’ should be ‘direct’.\nLine 98: ‘forest-resulted’ should be ‘forest-induced’\nLine 176: ‘northern mid-to-high latitudes’ is a bit confusing. Do you mean high latitudes in the northern hemisphere?\nLine 238: you mention PET-driven runoff decrease but the number in the parentheses is “+0.4%”. Do you mean “-0.4%”?\nLine 375: “...afforestation always reduces surface runoff” needs citation.\nFigure 1f: y-axis label is “PET/P - Dryness Index”. Maybe either dryness index or PET/P can be in parentheses as it now appears that you are subtracting the two terms.\nLine 580: “principal” should be “principle”\nLine 804: “Show” must be “Shown”?\nFigure 5: All occurrences of “Themodanymics” should be “Thermodynamics”.", "reviewer_2": "The manuscript by Fei Kan et al. addresses an important and timely topic: how global forestation influences runoff, with a particular focus on latitudinal divergence driven by forest-climate feedbacks. Using coupled land-atmosphere models and the Budyko framework, the authors find that forestation leads to increased runoff in tropical regions but reduced runoff in boreal regions. These findings underscore the importance of incorporating hydrological considerations into afforestation strategies aimed at climate mitigation. The manuscript is well-written and the technical details are carefully presented. However, I have several concerns regarding model uncertainty and methodological robustness that should be addressed before the manuscript can be considered for publication in Nature Communications.\nThis study bears strong resemblance to previous work on deforestation, particularly in terms of methodology and key findings. While the authors conduct additional analyses, the central conclusion—emphasizing the dominant role of forest-climate feedbacks and regional divergence—is not entirely novel. Beyond these points, are there any additional innovative insights that distinguish this study from prior literature?\nMa, S., Zhou, S., Yu, B. & Song, J. Deforestation-induced runoff changes dominated by forest-climate feedbacks. Science Advances 10, eadp3964 (2024)\nThe authors adopt a “full-potential forestation” scenario. Is this scenario similarly idealized as those used in earlier studies? How realistic is it under current and future climate and land-use constraints? Could comparable forestation pathways realistically occur in future scenarios? A critical issue is the reliability and accuracy of the simulated runoff responses. To what extent have these runoff outputs been validated against observations? If observational constraints are limited, how confident can we be in the attribution results derived from these model simulations?\nThe study primarily relies on IPSL-CM, with some additional analyses using CESM, to simulate forestation effects. Dependence on one or two models raises concerns about the robustness of the findings, especially given the relatively coarse spatial resolution of IPSL-CM. Prior studies have demonstrated substantial inter-model variability in forestation-induced changes in the water cycle: some models emphasize moisture recycling, while others highlight atmospheric circulation adjustments. Even within CMIP6 LUMIP experiments, large discrepancies exist among models under comparable scenarios. I recommend integrating CESM results more prominently into the main text and discussing model uncertainty in greater detail. It is difficult to place confidence in results derived primarily from a single model.\nDe Hertog SJ, et al. Effects of idealized land cover and land management changes on the atmospheric water cycle. Earth Syst Dynam 15, 265-291 (2024)\nPortmann, R. et al. Global forestation and deforestation affect remote climate via adjusted atmosphere and ocean circulation. Nat Commun 13, 5569 (2022).\nLawrence DM, et al. The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design. Geosci Model Dev 9, 2973-2998 (2016).\nLines 37–47: This section emphasizes carbon benefits, whereas the focus of this study is on the hydrological cycle. Please consider rebalancing the discussion to align with the core research objective.\nThe Budyko attribution approach is applied, yet the introduction lacks any description or justification of this method. Several statements in the introduction are not original contributions and should be supported with appropriate citations. The introduction discusses forestation-runoff relationships extensively but provides limited context on latitudinal divergence, which is central to this study.\nThe results section describes percentage changes, but some figures appear to display absolute numerical changes. Please clarify this inconsistency.\nFigure 1 reveals several unexpected findings that warrant more careful discussion. For example, precipitation changes exceed evapotranspiration changes in magnitude. This suggests that large-scale atmospheric circulation shifts and cascading moisture recycling effects may play a substantial role. In Southeast Asia, minor changes in evapotranspiration appear to trigger pronounced precipitation increases. However, these phenomena are not reproduced by CESM, highlighting the model dependence of the results. Supplementary Figure 4 statistics should be incorporated into the main text to strengthen the argument. Does CESM support the key findings of IPSL-CM simulations? The results for warm regions appear to differ considerably. The results for China in Figure 1d and Supplementary Figure 4d seem inconsistent with prior studies. Have the authors compared their findings with recent global forestation-water cycle simulations? There appear to be notable discrepancies.\nLi, Y. et al. Divergent hydrological response to large-scale afforestation and vegetation greening in China. Sci. Adv. 4, eaar4182 (2018)\nKing JA, Weber J, Lawrence P, Roe S, Swann ALS, Val Martin M. Global and regional hydrological impacts of global forest expansion. Biogeosciences 21, 3883-3902 (2024).\nThe use of the Penman-Monteith model for PET calculation could introduce potential biases. Recent studies suggest that the Penman equation overestimates PET because the assumption of a saturated surface is not satisfied over most land surface. I recommend considering the energy balance approach (PETe) instead.\nZhou, S. & Yu, B. Reconciling the Discrepancy in Projected Global Dryland Expansion in a Warming World. Glob Change Biol, 31: e70102 (2025).\nZhou, S., & Yu, B. Physical basis of the potential evapotranspiration and its estimation over land. Journal of Hydrology, 641, 131825 (2024).\nAdditionally, how have the authors accounted for internal climate variability in the Budyko attribution?\nThe Budyko attribution approach primarily considers first-order controls (mean climate and land surface characteristics) on runoff. Yet, forestation-induced changes in precipitation characteristics (e.g., rainfall intensity, fraction of precipitation as snow) may substantially alter runoff generation processes and soil moisture availability for evapotranspiration.\nFinally, can the authors provide observational evidence to support their model-based findings? Robustness checks relying solely on model-derived data are less persuasive; validation against independent observations is critical to establish confidence.", "reviewer_3": "I am pleased this subject is being explored as it is a poorly assessed aspect of the IPCC Reports. This manuscript should be accepted subject to satisfactory responses to the comments below. This is an excellent paper.\n1. The authors use the Budyko approach to estimate the integrated effects of precipitation, evapotranspiration (ET), potential evapotranspiration (PET) and landscape characteristics on aridity and runoff. This is a valuable analysis approach. However, while hydrologists use ET and PET, separating into physical evaporation (such as from soils and water bodies) and transpiration from plants through stoma would strengthen (and likely add a new perspective to) the analysis. I recommend added text on this wrt needed follow on research.\nHere are papers that discuss this issue\nMiralles, D.G.; Brutsaert, W.; Dolman, A.J.; Gash, J.H. On the Use of the Term “Evapotranspiration”. Water Resour. Res.2020, 56\nPielke Sr., R.A., D. Peters, and D. Niyogi, 2022: Ecology and climate of the Earth - the same biogeophysical system. Climate, 10, 25, https://doi.org/10.3390/cli10020025\n2. There has been significant greening over the last decades. Have the authors considered this? I recommend discussing.\nZhu, Z.; Piao, S.; Myneni, R.B.; Huang, M.; Zeng, Z.; Canadell, J.G.; Ciais, P.; Sitch, S.; Friedlingstein, P.; Arneth, A.; et al. Greening of the Earth and its drivers. Nat. Clim. Change2016, 6, 791–795\n3. The spatial resolution of their models is too coarse to evaluate accurately the role of most regional spatial heterogeneity, and no mesoscale landscape structure.\nPatchiness of deforestation nonlinearly affects the amount and intensity of cumulus development, for example, and thus rainfall and then runoff. In high latitudes it affects snow melt also.\nHere are two studies of that effect.\nAvissar, R., and Y. Liu, Three-dimensional numerical study of shallow convective clouds and precipitation induced by land surface forcing, J. Geophys. Res., 101, 7499–7518, 1996.\nAnthes 1984: Enhancement of convective precipitation by mesoscale variations in vegetative covering in semiarid regions. J. Clim. Appl. Meteor., 23, 541-554\n4. Their resolution of 1.26 degrees by 2.5 degrees and 1.9 degrees by 2.5 degrees are obviously insufficient to resolve features smaller than 2 delta X ( more accurately 4 delta X).\nIndeed, forestation that is spaced on spatial scales smaller than that is likely to result in an enhancement of precipitation, than a uniform cover as cited earlier. The authors should recommend a follow up study with high spatial resolution global climate models. They don’t need to be long term or equilibrium runs.\n5. The authors might find it useful to cite a 2005 NRC report which, while essentially ignored by the IPCC, has executive findings that advocated for the type of study presented in the submitted manuscript\nNational Research Council, 2005: Radiative forcing of climate change: Expanding the concept and addressing uncertainties. Committee on Radiative Forcing Effects on Climate Change, Climate Research Committee, Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies, The National Academies Press, Washington, D.C., 208 pp.\nHere are excerpts from the findings\n“Regional variations in radiative forcing may have important regional and global climatic implications that are not resolved by the concept of global mean radiative forcing. Tropospheric aerosols and landscape changes have particularly heterogeneous forcings. To date, there have been only limited studies of regional radiative forcing and response. Indeed, it is not clear how best to diagnose a regional forcing and response in the observational record; regional forcings can lead to global climate responses, while global forcings can be associated with regional climate responses. Regional diabatic heating can also cause atmospheric teleconnections that influence regional climate thousands of kilometers away from the point of forcing. Improving societally relevant projections of regional climate impacts will require a better understanding of the magnitudes of regional forcings and the associated climate”\nand\n“Several types of forcings—most notably aerosols, land-use and land-cover change, and modifications to biogeochemistry—impact the climate system in nonradiative ways, in particular by modifying the hydrological cycle and vegetation dynamics. Aerosols exert a forcing on the hydrological cycle by modifying cloud condensation nuclei, ice nuclei, precipitation efficiency, and the ratio between solar direct and diffuse radiation received. Other nonradiative forcings modify the biological components of the climate system by changing the fluxes of trace gases and heat between vegetation, soils, and the atmosphere and by modifying the amount and types of vegetation. No metrics for quantifying such nonradiative forcings have been accepted. Nonradiative forcings have eventual radiative impacts, so one option would be to quantify these radiative impacts. However, this approach may not convey appropriately the impacts of nonradiative forcings on societally relevant climate variables such as precipitation or ecosystem function. Any new metrics must also be able to characterize the regional structure in nonradiative forcing and climate response”\nSee also\nKabat, P., Claussen, M., Dirmeyer, P.A., J.H.C. Gash, L. Bravo de Guenni, M. Meybeck, R.A. Pielke Sr., C.J. Vorosmarty, R.W.A. Hutjes, and S. Lutkemeier, Editors, 2004: Vegetation, water, humans and the climate: A new perspective on an interactive system.Springer, Berlin, Global Change - The IGBP Series, 566 pp.\n6. I suggest other studies that further support their findings\nHere are just a few examples (and references therein)\nBetts, R. A. 2000 Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature 408, 187-189.\nHossain, F., J. Arnold, E. Beighley, C. Brown, S. Burian, J. Chen, S. Madadgar, A. Mitra, D. Niyogi, R.A. Pielke Sr., V. Tidwell, and D. Wegner, 2015: Local-to-regional landscape drivers of extreme weather and climate: Implications for water infrastructure resilience. J. Hydrol. Eng., 10.1061/(ASCE)HE.1943-5584.0001210 , 02515002.\nPielke, R.A. Sr., J. Adegoke, A. Beltran-Przekurat, C.A. Hiemstra, J. Lin, U.S. Nair, D. Niyogi, and T.E. Nobis, 2007: An overview of regional land use and land cover impacts on rainfall. Tellus B, 59, 587-601, DOI:10.1111/j.1600-0889.2007.00251.x\nPielke Sr., R.A., A. Pitman, D. Niyogi, R. Mahmood, C. McAlpine, F. Hossain, K. Goldewijk, U. Nair, R. Betts, S. Fall, M. Reichstein, P. Kabat, and N. de Noblet-Ducoudré, 2011: Land use/land cover changes and climate: Modeling analysis and observational evidence. WIREs Clim Change 2011, 2:828–850. doi: 10.1002/wcc.144.\nPielke Sr., R.A., R. Mahmood, and C. McAlpine, 2016: Land’s complex role in climate change. Physics Today, 69(11), 40, https://doi.org/10.1063/PT.3.3364\nRay, D.K., U.S. Nair, R.O. Lawton, R.M. Welch, and R.A. Pielke Sr., 2006: Impact of land use on Costa Rican tropical montane cloud forests. Sensitivity of orographic cloud formation to deforestation in the plains. J. Geophys. Res., 111, D02108, doi:10.1029/2005JD006096.\nI look forward to seeing the publication of this manuscript. I anticipate it will be widely cited"}, "author_response": "Point-to-point responses to reviewer comments\nWe thank the three reviewers for their careful review and very helpful\ncomments/suggestions. Below comments from these reviewers are copied, followed\nby our detailed responses in blue. Additional or altered text in the revised MS, when\ncited in this response document, is marked in red italics.\n[Reviewer #1 General Comment]\nIn this study, Kan et al. conduct a fairly comprehensive Earth system modelling\n(ESM)-based experiment to assess the impact of global forestation on runoff. For this,\nin addition to a control simulation, they run the ESMs with the tree restoration\npotential map from Bastin et al. 2019. They combine the bottoms-up modelling with a\nsimple top-down Budyko framework to partition the drivers of runoff changes among\natmospheric and land processes. While the paper is well-written and the experiment is\nlogically designed, I have some reservations about the use of the Bastin et al. dataset\nand the over-interpretation of the Budyko framework. As a result, I suggest major\nrevisions before the manuscript can be considered for publication in\nNature Communication. I detail my concerns below.\n[Response] We highly appreciate your positive evaluation of our MS, and are pleased\nto hear that you find the experiment logically designed, the writing clear, and the\nanalysis comprehensive. Your recognition of the significance of the findings and the\nextension they offer to earlier work in the field is particularly encouraging. Below, we\nprovide details on how we tackled your raised issues and how these have contributed\nto the refinement of our MS. We believe that these revisions have greatly improve the\nrobustness and quality of the MS.\n[Reviewer #1 Major Comment 1]\n1. Although I understand to some extent the logic behind using the Bastin et al. 2019\ndata, I am not sure if it represents the “maximum potential” as the authors describe.\nMost criticisms of the dataset make it clear that the tree restoration potential has been\nvastly overestimated. Hence, this cannot be considered as the potential. I request the\nauthors to motivate the use of this dataset better. One possible option is to do the\nanalysis for various degrees of restoration potentials but I am not sure how\ncomputationally feasible this option is. If not, this represents a weakness in the\nexperiment design and the results are not exactly a realistic scenario, which the\nauthors are aiming for.\n[Response] We appreciate your constructive comment regarding the potential\noverestimation of actual afforestation potential by using the Bastin’s dataset1. To\naddress this important concern and better motivate the use of the dataset, we have set\nup three potential forestation scenarios based on the Bastin’s dataset:\nThe first scenario is the full-potential forestation scenario (Bastin_Full), which\nassumes planting trees to reach the land’s theoretical maximum carrying capacity\nunder current climate conditions, representing the idealized maximum afforestation\npotential.\nThe second scenario, Bastin_Refined_Climate, is based upon Bastin_Full but\nexcludes areas with frequent fire and regions with additional climate constraints due\nto forest-induced albedo changes. This scenario addresses two major critiques of the\nBastin’s data: the overestimation of tree cover potential in grassland and savanna\necosystems with frequent fire, and the modeling of new tree cover in water-limited\nregions where trees reduce albedo and induce net climate warming2–4. Applying these\nconstraints lead to a lower estimated afforestation potential, particularly in tropical\ngrassland and savanna ecosystems across Africa (Fig. R1b).\nFurthermore, the third scenario, Bastin_Refined_Climate&Land, is an extended\nversion of Bastin_RefinedClimate that additionally excludes croplands, built-up lands\nand wetlands. This represents the most feasible forestation scenario. We excluded\nurban and agricultural areas due to their critical importance for human sustenance and\nsocio-economic activities. We applied the wetland precautionary exclusions to protect\nlarge soil organic carbon (SOC) stocks that may be vulnerable to losses following the\nestablishment of trees5,6. Compared with the Bastin_Refined_Climate scenario, the\nfurther reduction in afforestation area in Bastin_Refined_Climate&Land\npredominantly occurs in cropland-dominated regions like Europe, African croplands,\nSoutheast Asia, and eastern South America (Fig. R1c).\nFig. R1 (also appears as Supplementary Fig. 1 in the revised version) Spatial pattern\nof tree fraction changes (∆𝑇𝑟𝑒𝑒 𝑐𝑜𝑣𝑒𝑟) due to global forestation under a hierarchy of\nprogressively constrained scenarios: (a) Bastin_Full scenario, (b)\nBastin_Refined_Climate scenario, (c) Bastin_Refined_Climate&Land scenario.\nThese three increasingly constrained scenarios offer a range of possible afforestation\nplans, from the idealized maximum to the most realistic one, allowing us to explore\nthe robustness of our results across different afforestation schemes. The Bastin_Full\nscenario serves as the baseline, while the other two constrained forestation scenarios\nare designated as sensitivity tests. Specific details on the sensitivity scenario settings\nand the exclusionary datasets used can be found in the revised Methods section,\nwritten as:\n“Two sensitivity experiments with constrained forestation\nAs previously noted, the Bastin et al.’s data likely overestimate potential tree cover;\nthus, our full-potential forestation scenario (Bastin_Full) represents the upper bound\nof global forestation potential. In this regard, we designed two additional scenarios\n(Bastin_Refined_Climate and Bastin_Refined_Climate&Land) by progressively\nintroducing more realistic constraints to test the robustness of our results. These three\nscenarios represent a range of possible forestation pathways, from the theoretical\nmaximum to the most practical feasible.\nBastin_Refined_Climate builds upon Bastin_Full by excluding regions with frequent\nfires and where forestation would produce net warming due to reduced surface\nalbedo. This refinement addresses two major critiques of the Bastin et al.’s dataset:\noverestimated potential in fire-prone grassland and savanna ecosystems, and the\nneglect of forestation-caused regional warming due to reduced albedo65,66. Following\nFesenmyer et al.64, we used the 500 m MODIS Burned Area dataset to remove all non-\ncropland areas experiencing two or more fires during 2002–2022, assuming that such\nfrequencies substantially limit tree density114. We further excluded regions where\nalbedo-induced radiative warming exceeds the cooling from carbon sequestration of\npotential forestation, based on the dataset of Halser et al.115. These constraints\nmarkedly reduce the estimated afforestation potential in tropical grassland and\nsavanna ecosystems, particularly across Africa (Supplementary Fig. 1b).\nBastin_Refined_Climate&Land incorporated additional constraints on\nBastin_Refined_Climate by excluding croplands, built-up areas, and wetlands to\nensure land-use sustainability and feasibility. Urban and agricultural areas, given\ntheir essential roles in food production and human livelihoods, were masked out using\nthe ESA CCI Global Land Cover dataset for 2000. Wetlands were excluded using the\nGlobal Lakes and Wetlands Database (GLWD, version 2) to prevent potential losses\nof large soil organic carbon (SOC) stocks following afforestation. Compared with the\nBastin_Refined_Climate scenario, the further reduction in forestation potential in\nBastin_Refined_Climate&Land predominantly occurs in cropland-dominated regions\nlike Europe, African croplands, Southeast Asia, and eastern South America\n(Supplementary Fig. 1c).” (Lines 627-657).\nBoth further constrained scenarios (Bastin_Refined_Climate and\nBastin_Refined_Climate&Land) exhibit a latitudinal pattern of runoff responses\nsimilar to that of the Bastin_Full scenario, with increases in the tropics and reductions\nat high latitudes (Fig. R2). Using the Budyko framework to attribute forestation-\ninduced runoff changes, we find that the dominant role of forest-atmosphere\nfeedbacks in shaping the latitudinal gradient of runoff responses remains robust across\ndifferent afforestation scenarios (Fig. R3). While the overall pattern remains\nconsistent, discrepancies are present in regional responses among the scenarios. In\nBastin_Refined_Climate, reduced forestation area over tropical grasslands and\nsavannas leads to less strong tropical runoff increases. In\nBastin_Refined_Climate&Land, the further exclusion of cropland and urban areas\nweakens, but not reverses, the decrease runoff in some high-latitude regions. We have\nincorporated the new results from these additional sensitivity tests into the revised\nmanuscript, as follows:\n“Since the full-potential afforestation scenario using the Bastin et al.’s dataset may\noverestimate the actual reforestation potential64–66, we further incorporated additional\nconstraints on the potential forestation maps to test the robustness of results. Building\non Bastin_Full, a new scenario (Bastin_Refined_Climate) additionally excludes areas\nwith frequent fire occurrences or with expected albedo-induced regional warming\nafter afforestation, and another new scenario (Bastin_Refined_Climate&Land)\nfurther excludes croplands, built-up lands, and wetlands. Our simulations forced by\nrefined potential forestation maps produced a latitudinal gradient of runoff responses\nsimilar to that of Bastin_Full, though with regional difference in the hydrological\neffects (Supplementary Fig. 5). Compared with Bastin_Full, reduced forestation area\nover tropical grasslands and savannas leads to less strong tropical runoff increase;\nand further exclusion of afforestation over croplands and urban areas in\nBastin_Refined_Climate&Land show less significant decreases in high-latitude\nregions and weaker increases in low-latitude regions. (Supplementary Figs. 1 and 5).\nIn both afforestation scenarios, the contrasting pattern of enhanced tropical runoff\nand suppressed high-latitude runoff remains evident.” (Lines 189-203);\n“Furthermore, the two further constrained forestation scenarios consistently show\nthat forest-atmosphere feedbacks strongly shape the latitudinal gradient of runoff\nresponses (Supplementary Fig. 7).” (Lines 241-243).\nFig. R2 (also appears as Supplementary Fig. 5 in the revised version) Forestation-\ninduced runoff changes (ΔR) across the two additionally constrained forestation\nscenarios. (a) Bastin_Refined_Climate scenario, (b) Bastin_Refined_Climate&Land\nscenario.\nFig. R3 (also appears as Supplementary Fig. 7 in the revised version) Runoff changes\ncaused by indirect forest-atmosphere feedbacks and direct land surface effects of\nglobal potential forestation across the two additionally constrained forestation\nscenarios. (a, b) Spatial patterns of runoff changes associated with forest-atmosphere\nfeedbacks (∆𝑅 ) in Bastin_Refined_Climate scenario (a) and\n𝑎𝑡𝑚\nBastin_Refined_Climate&Land scenario (b). (c, d) The same with (a, b) but for land\nsurface effects (∆𝑅 ).\n𝑙𝑎𝑛𝑑\n[Reviewer #1 Major Comment 2]\n2. Another source of uncertainty and one that is fully acknowledged in the study is the\nissue of fixed rooting depths prescribed in the ESMs. Even if the rooting depths show\ninter-pft variability, there is large variance intra-pft and this would cause a lot of\nspatial variability in soil water uptake. I acknowledge that this is not something that\ncan be easily addressed but should, ideally be discussed. For example, In line 337, the\nauthors say that ET is enhanced but with shallower root depths, the vegetation might\nnot have the capacity to access potentially depleted soil moisture.\n[Response] Thank you for raising the criticism of Earth System Models (ESMs)\nregarding their fixed rooting depths for specific plant functional types (PFTs), which,\nwe agree, is a key source of uncertainty in eco-hydrological modeling. The rooting\ndepth is known to vary considerably across gradients of hydroclimate, soil properties,\nand topography7–9. For example, plants typically develop deeper roots in arid regions\n8,9—a strategic adaptation to access deeper soil moisture, which contrasts with their\ninvestment in aboveground growth in water-abundant areas10. In our model-based\nglobal forestation experiment, the fixed rooting depth cannot capture the growth\ndynamic of roots, and thus may overestimate the actual root depth of newly\nestablished trees. This bias may propagate to an overestimation of the forestation-\ninduced increase in evapotranspiration (ET) and the associated soil drying,\nparticularly in wet regions such as Southeast Asia. In the revised manuscript, we have\nincluded a few sentences in Discussion to highlight the limitation of fix root depth,\nas detailed below:\n“Second, our simulations do not account for the spatial and temporal dynamics of tree\nrooting depths, which are prescribed as fixed values for each plant functional type\n(PFT) in land surface models. Hence, the models do not represent how rooting depths\nvary with hydroclimates, soil properties, topography, and forest age94–96. This\nsimplification may lead to biases, since observations show that the rooting depth tend\nto increase with aridity to access deeper water sources95,96, and evolve over a tree’s\nlifetime97. For instance, in humid regions like Southeast Asia, using a fixed,\npotentially overestimated rooting depth for newly planted trees could overestimate the\nprojected increase in ET and the consequent soil water depletion (Fig. 1).” (Lines\n518-526)\n[Reviewer #1 Major Comment 3]\n3. This is not exactly a suggestion but more of an observation: it is interesting that the\nrunoff increases in the tropics and decreases in the boreal regions. As the boreal\nregions are fairly humid, it would be interesting to see if they go past a key\naridity threshold (humid to dry-subhumid for example).\n[Response] Thank you for sharing this insightful perspective. To test whether\nforestation-reduced runoff in boreal regions would shift the regional aridity state, we\nlink the hydrological responses directly to aridity conditions using the Aridity Index\n(AI, the ratio of precipitation [P] to potential evapotranspiration [PET]) (Fig. R4).\nFollowing the United Nations Environment Programme (UNEP) classification, an AI\n(P/PET) greater than 0.65 denotes humid conditions, and an AI between 0.5 and 0.65\nindicates dry sub-humid conditions.\nOur results indicate that boreal regions in Eurasia remain to be classified as a humid\nregion (AI > 0.65) following large-scale forestation, with the spatial distribution of the\nhumid and dry sub-humid areas being almost unchanged. This suggests that while\nforestation does reduce runoff in boreal regions, it does not trigger a shift in the\nregion’s climatic state from humid to dry sub-humid.\nFig. R4 Spatial patterns of dryness conditions in CTL (a) and SCE (Bastin_Full\nscenario, b). Colors represent regions of different aridity levels classified using an\naridity index according to the United Nations Environment Programme (UNEP).\n[Reviewer #1 Major Comment 4]\n4. Much of the results and the consequent conclusions depend on how good the\nhydrological model within ORCHIDEE is? Considering that LSMs are not intended\nfor hydrological modelling, the authors should provide more details on how the\nrunoff from the model is validated, and to what degree can they be trusted? In this regard the\nmanuscript is very light on the details.\n[Response] We sincerely thank you for this comment which motivated us to evaluate\nthe mode performance in simulating runoff. In light of your comment, we conducted a\nvalidation of the simulated runoff against observed streamflow data derived from the\nGlobal Streamflow Indices and Metadata Archive (GSIM)11,12. In the Supplementary\nText 3, we provide details on this streamflow database and describe the data screening\ncriteria and processing, as also copied here:\n“We conducted a validation of the simulated runoff using streamflow observations\nfrom the Global Streamflow Indices and Metadata Archive (GSIM)1,2, which provides\ndaily records of streamflow from over 30,000 stations worldwide. To ensure\ncomparability with model’s coarse spatial resolution and its lack of representation of\ndirect human interventions (e.g., dam construction, irrigation), we screened the\navailable catchments based the following criteria: (1) with a drainage area greater\nthan 20,000 km2; (2) with at least 15 years of complete annual streamflow records\nbetween 1971 and 2010; (3) with ‘high’ or ‘medium’ boundary quality, indicating less\nthan 10% difference between the calculated and reported drainage area; (4) with\nirrigation areas < 1% of total area or reservoir capacity < 10% of mean annual\nstreamflow; (5) Only catchments for which four homogeneity tests (i.e., the standard\nnormal homogeneity test3, the Buishand range test4, the Pettitt test5, and the von\nNeumann ratio test6, adopted by the GSIM data) all fail to reject the null hypothesis of\nno break-point in the time series at the 1% significance level were selected. The\ncatchmental irrigation area and reservoir capacity, and quality control for catchment\nboundaries, are all derived from the GSIM metadata.\nApplying these criteria led to a total of 270 catchments. The locations of these\ncatchments and the number of valid annual observations are shown in the\nSupplementary Fig. 26a. For comparability with models’ runoff output, mean annual\nrunoff (in mm day-1) from observations were obtained by dividing the river discharge\n(m3 day-1) by the station’s upstream catchment area (km2). For the simulated runoff by\nIPSL and CESM (based on the CTL run), we first interpolated model output to a 1°\nresolution. For each selected catchment, the catchment-scale runoff was calculated as\nthe areal-weighted average of grid cells falling within the catchment.”\n(Supplementary Text 3).\nFig. R5 (also appears as Supplementary Fig. 26 in the revised version) Supplementary\nFig. 26 Evaluation of the model-simulated mean annual runoff across the 270\ncatchments. (a) Spatial distribution of the 270 selected catchments and the number of\nyears with available observations per catchment. (b, c) Scatter plots of annual mean\nrunoff simulated by IPSL (b) and CESM (c) versus corresponding observations across\nthe 270 catchments. The size of the markers represents the catchment area, and the color\nof the markers denotes the number of available observation years for each catchment.\n(d) Box plots of the difference between modelled and observed annual mean runoff,\nshowing the median (horizontal lines), 25th, 75th quartiles, maximum and minimum\nvalues (top and bottom of the whiskers), outliers (dots) and the 95% confidence interval\nfor the median (notches).\nUsing streamflow observations from the 270 selected catchments (as shown in Fig.\nR5a), we evaluated the performance of modelled runoff. As expected, IPSL-\nsimulated runoff exhibits notable deviations from observed values especially in large\ncatchments (Fig. R5b). This is because IPSL does not account for the effects of\nurbanization, anthropogenic land-use changes, and river routing processes within the\ncatchments particularly those large ones. Despite this difference for specific\ncatchments, we find an overall significant positive correlation (r = 0.66, p < 0.01)\nbetween the IPSL simulations and observations (Fig. 5b), indicating that IPSL\nsuccessfully captures characteristics of runoff in ‘wet’ versus ‘dry’ catchments.\nImportantly, across all available catchments, the median difference between IPSL\nestimates and observations is significantly indistinguishable from zero (Fig. 5d),\nsuggesting no systemic bias in IPSL simulations of runoff at the continental scale.\nFurthermore, we find that IPSL outperforms CESM in runoff simulations. Evaluation\nof CESM-based estimates show a weak, though significant, correlation (r) of 0.20 (p\n< 0.01) (Fig. R5c), and a significant negative deviation of the modelled runoff from\nobservations, indicating a systemic underestimation of runoff (Fig. R5d). Given this\napparent discrepancy, we chose not to present the CESM-based estimates in the main\nfigures, but instead include this as an additional model test to verify our primary\nfindings based on IPSL. In the revised manuscript, we presented the evaluation of\nmodel performance based on streamflow observations. The added text reads as\nfollows:\n“Model performance was further evaluated using observed streamflow records of 270\nscreened catchments from the Global Streamflow Indices and Metadata Archive\n(GSIM)109,110 (Supplementary Text 3). The IPSL-CM successfully reproduces cross-\ncatchment runoff variability (r = 0.66, p < 0.01), with the median of model-\nobservation difference statistically indistinguishable from zero. This indicates no\nsystematic bias in the IPSL-modelled runoff, supporting the model capability for\nassessing afforestation-induced runoff responses (Supplementary Fig. 26).” (Lines\n558-564 in revised manuscript);\n“It is worth noting that while CESM-simulated runoff is significantly correlated with\nobservations, CESM overall underperforms IPSL (Supplementary Fig. 26). We\ntherefore present the CESM-based results in the Supplementary Information to\nsupport the robustness of our primary findings from IPSL.” (Lines 621-625 in revised\nmanuscript).\n[Reviewer #1 Major Comment 5]\n5. One of the biggest advantages of the study is the fully coupled model which\nsimulates the remote effects of local forestation, and which is very nicely shown in\nthe study. However, the authors predominantly focus on moisture recycling.\nHow about the impact of forestation on albedo changes and potential subsequent changes in\nthe atmospheric circulation? Does the increased atmospheric moisture affect the\nstability of the atmosphere, thus affecting precipitation? In this regard, I find the\ndiscussion of the results very sparse and superficial.\n[Response] Thank you very much for recognizing the strength of our model approach\nin capturing the remote effects of forestation, and for your suggestion of discussing\nhow afforestation influences precipitation by modifying atmospheric circulation. We\nacknowledge that our original MS only addressed the influence of atmospheric\ndynamics on precipitation through moisture budget decomposition, and the energy\nbalance perspective could help further understand the drivers.\nFirst, we explored the influence of forestation-altered surface energy balance\n(particularly reduced albedo, as shown in Fig. R6a) on atmospheric circulation\npatterns. It should be noted that forestation alters surface energy balance by\nmodifying several key land surface characteristics, including albedo,\nevapotranspiration and surface roughness. The combined changes in these\nbiogeophysical factors result in localized diabatic heating or cooling of the lower\natmosphere13. This modification in the thermal structure of the atmosphere further\ninitiates a stationary Rossby wave train (Fig. R6b), ultimately leading to the large-\nscale circulation reorganization as documented in our original MS (Fig. R7).\nFor example, a large anticyclonic anomaly forms over Eurasia (Fig. R7c), which\nweakens the prevailing westerlies and thus suppresses the advection of moisture from\nthe North Atlantic, consequently inhibiting advective precipitation (Fig. R8b).\nConversely, the southeasterly winds over Southeast China are strengthened, enhancing\nmoisture transport from the ocean and consequently favoring advective rainfall in that\nregion (Fig. R7c and R8b). In the revised version, we have expanded the discussion of\nthis energy balance aspect, as detailed below:\n“The alteration in large-scale atmospheric circulation may be associated with\nforestation-induced changes in surface energy balance, mainly through reduced\nalbedo44,51, which modify the thermal structure of the lower atmosphere and further\ninitiates a stationary Rossby wave train (Supplementary Fig. 25).” (Lines 497-500);\n“For example, the southeasterly winds over Southeast China are strengthened,\nenhancing moisture transport from the ocean and consequently favoring advective\nrainfall in that region (Supplementary Figs. 14b and 15c).” (Lines 314-317);\n“In addition, the large anticyclonic anomaly induced by forestation over Eurasia\nweakens the prevailing westerlies, thereby reducing moisture advection from the\nNorth Atlantic and inhibiting advective precipitation. (Supplementary Figs. 14b and\n15c).” (Lines 333-336).\nSecond, we examined how forestation affects precipitation by changing atmospheric\nstability. Actually, our previous manuscript has implicitly mentioned this mechanism.\nIn our moisture budget decomposition, we noted that afforestation-induced increased\natmospheric moisture raises the vertical moisture gradient (Fig. R7b), which enhances\nupward moisture transport and facilitates the development of convective precipitation\n(Fig. R7c and Fig. R8c). This strengthening upward motion indirectly corresponds to\na reduction in atmospheric stability. In the revised manuscript, we have additionally\nemployed the Convective Available Potential Energy (CAPE) metric to provide a\nmore direct and intuitive assessment of the changes in vertical stability and the\npotential for convective development. Our new results demonstrate that the enhanced\nnear-surface moisture significantly increases CAPE by altering the vertical profile of\natmospheric moist static energy, thus providing more energy for vigorous convective\nupdrafts (Fig. R7a). In the revised manuscript, we have further elaborated on how\nafforestation influences convective precipitation by changing atmospheric stability.\nThe added text reads as follows:\n“This is because forestation significantly enhances the Convective Available Potential\nEnergy (CAPE) and the vertical gradient of specific humidity (Supplementary Fig.\n15a, b), indicative of reduced atmospheric stability, thereby promoting the upward\nmotion of moisture (Supplementary Fig. 15c) and increasing convective precipitation\n(Supplementary Fig. 14c).” (Lines 306-310)\nFig. R6 (also appears as Supplementary Fig. 25 in the revised version) Global\nforestation-induced changes in surface albedo (a) and atmospheric circulation patterns\n(b). In b, the shading and vectors indicate forestation-induced changes in 500 hPa\ngeopotential height and Plumb flux, respectively.\nFig R7 (also appears as Supplementary Fig. 15 in the revised version) Spatial patterns\nof global forestation-induced changes in atmospheric processes. (a) Changes in\nConvective Available Potential Energy (CAPE, unit: J-1 kg-1) (b) Changes in vertical\ngradient of specific humidity (unit: g kg-1 hPa-1), calculated as the linear regression\ncoefficient of specific humidity against atmospheric pressure across the vertical\nprofile. (c) Changes in vertical velocity (shading, unit: Pa s-1) and horizontal winds\n(vectors, unit: m s-1). These two variables are obtained by vertically integrating the\nvertical velocity and wind at each level, respectively, using specific humidity as the\nweighting factor. Black stipples in a and b denote statistically significant change at the\n5% significance level using the paired sample t-test.\nFig. R8 (also appears as Supplementary Fig. 14 in the revised version) Moisture\nbudget decomposition of IPSL-simulated precipitation changes. Spatial patterns of\nglobal forestation-induced precipitation changes associated with different atmospheric\nprocesses, including thermodynamic effect (ΔWV, a), modified vertical moisture\nadvection (ΔVert, b), altered horizontal moisture advection (ΔAdv, c), and land\nsurface ET changes (ΔET, d). Res (e) represents the precipitation changes that cannot\nbe explained by the four additive processes. Black stipples denote statistically\nsignificant change at the 5% significance level using the Student’s t-test.\n[Reviewer #1 Major Comment 6]\n6.Calling the Budyko parameter as “water retention capacity” is not exactly correct.\nThe partitioning of precipitation into evaporation is not only because more water is\nstored in the watershed but also due to lateral flow out of the watershed. The\nparameter is more an integral of all the factors that could not be explained by the\noriginal non-parametric Budyko hypothesis. Maybe a better phrase is “partitioning\nfactor”?\n[Response] We are grateful for this thoughtful comment on our interpretation of the\nBudyko parameter n. We agree that, in the Budyko framework, n is a highly\nsophisticated parameter that has integrated effects of many land surface processes14,15.\nRespectfully, we contend that referring to the shape parameter n as a “partitioning\nfactor” is somehow misleading, as n was originally introduced to represent the\ncatchment’s physical properties beyond climate forcing16,17. Specifically, the\npartitioning of precipitation into evapotranspiration (i.e., ET/PET) is jointly controlled\nby climatic conditions (precipitation, PET) and n, as expressed by the Budyko\nequation (Equation 1). Hence, the terminology “partitioning factor” may cause\nconfusion about n and ET/PET. This is also the reason for the rare use of this\nterminology in the existing Budyko-related literature.\n1\n𝐸𝑇 𝑃𝐸𝑇 −𝑛 − 𝑛\n(1)\n= [( ) +1]\n𝑃 𝑃\nActually, the interpretation of the shape parameter n as “water retention capacity” is\nwell-justified and widely adopted in the eco-hydrology literature14,15,18–20. Of course,\nwe acknowledge that most eco-hydrological studies, including those cited, do not\nexplicitly account for lateral flows between adjacent catchments. Furthermore, there is\nlittle evidence to confirm whether accounting for such lateral water movement would\nsignificantly alter Budyko-based hydrological assessments. Therefore, and to\nincorporate your perspective, and to reflect the comprehensive nature of this shape\nparameter, we chose to use a more general interpretation of n, referring to it as the\n“surface parameter representing catchment characteristics”. In the revised manuscript,\nwe have explicitly clarified the meaning of this parameter and its connection of the\nwatershed’s water retention capacity. The amended texts are:\n“This more universal negative effect of ∆𝑅 in mid-to-low latitudes arises from the\n𝑙𝑎𝑛𝑑\nforestation-induced increase in the surface parameter n (Supplementary Fig. 13b, d),\nrepresents catchment characteristics governing runoff generation, and primarily\nreflect the water retention capacity of the catchment26,60,75–77. The consistent increase\nin n indicates that afforestation-induced catchmental changes overall enhances\nrainfall portioning into regional ET, thereby reducing runoff.” (Lines 368-373)\n[Reviewer #1 Major Comment 7]\n7. In line with the previous comment, I feel that the utility of the Budyko hypothesis\nhas been stretched in the study. First, it was not originally intended to be used at grid-\ncell level but more at large catchment-scale, and at very long-term timescales (greater\nthan 20–30 years). While a lot of studies have over-used the framework, I am not\nexactly sure how to suggest improvements here but at the very least, these limitations\nhave to be acknowledged. For example, line 215: The applicability of the hypothesis\nin cold regions is questionable.\n[Response] Thank you for sharing your perspectives about the Budyko hypothesis.\nYes, applying the Budyko framework to grid-scale analysis is an extension of its\noriginal intent. The rationale behind is, in the land surface model, runoff generation is\ncomputed independently for each grid cell as a partitioning of rainfall, without\nrepresentation of lateral flow between cells21–23. In this context, each grid cell\nfunctions as a small, hydrologically independent unit, therefore the Budyko theories\nestablished and validated at the catchmental scale are also appliable to model’s grid\ncells. This grid-cell application is extensive in the literature for separating the effects\nof climate or land surface changes on surface runoff at regional to global scales24–26.\nWith respect to the requirement for long-term timescales, as you mention, we agree\nthat our previous 30-year analysis period might not be sufficiently long. In light of\nyour comment, we have extended the period of analysis to a longer 60-year timescale.\nThis extended 60-year period not only satisfies the steady-state assumption of the\nBudyko framework, but also reduces the influence of inter-decadal or multi-decadal\nclimate variability on our attribution. The updated results based on the last 60 years\nare consistent with the previous findings: the atmosphere-forest feedback dominates\nthe latitudinal divergence of the forestation-induced runoff response (please refer to\nthe updated main figures in the revised manuscript).\nWith respect to your concern about the applicability in cold regions, we agree that the\nattribution using the Budyko framework might be biased over there. In these regions,\na considerable portion of precipitation falls as snow during winter, and subsequent\nsnowmelt in spring and early summer substantially contributes to seasonal runoff\ngeneration. Critically, the effects of such snow-related processes, or more generally\nprecipitation characteristics (as see Reviewer #2’s Major Comment 9), are not\nexplicitly represented in the traditional Budyko framework. Specifically, in the\nBudyko framework, the effects of precipitation characteristics (including snow-related\nprocesses) on runoff were implicitly incorporated into the parameter n27, biasing our\ninterpretation of n changes as resulting from land surface processes. This provides the\nrationale for the limited applicability in cold regions, as you point out.\nTo address potential bias related to precipitation characteristics, we separated\nprecipitation and runoff changes due to altered precipitation characteristics and\nincorporated these into effects of atmospheric changes, following the approach\nproposed by Zhou et al.25. We further decomposed the effects of the atmospheric\nfeedbacks (due to altered mean precipitation, PET, and precipitation characteristics),\nand land surface changes (only due to catchment properties) on runoff under the\nmodified Budyko framework. In particular, this modification ensures applicability in\ncold regions because snow-related biases have been corrected. The results based on\nthis extended method are consistent with our main conclusions, showing that forest-\natmosphere feedbacks primarily drive the latitudinal contrast in afforestation-induced\nrunoff changes, with decreases at high latitudes and increases in the tropics (Fig. R9).\nIn the revised MS, we have added a detailed description of this Budyko attribution\nmethod with correction to effects of precipitation characteristics and of how this\nmodification supports our main conclusions. The added text reads as follows:\n“Finally, we assessed the potential influence of forestation-induced changes in\nprecipitation characteristics (e.g., rainfall intensity, snowfall fraction) on the Bydyko-\nbased attribution of runoff changes. In cold regions, for example, a considerable\nportion of precipitation falls as snow during winter, and the subsequent spring or\nearly-summer snowmelt substantially contributes to seasonal runoff. Since the\nstandard Budyko framework only accounts for the first-order control of mean climate\n(precipitation and PET) and land surface conditions, the effects of precipitation\ncharacteristics on runoff (e.g., through the inter-seasonal processes) were implicitly\nconflated into the data-approximated n (ref.69). Hence, this may bias our\ninterpretation of n changes as resulting from altered land surface properties. To\naddress this bias, we adopted a modified Budyko-based approach for attribution\nfollowing Zhou et al.61, which isolates the portion of precipitation change solely\ndriven by altered precipitation characteristics (𝑃∗). In this approach, the 𝑃∗-related\nportion of precipitation is artificially partitioned into runoff and directly subtracted\nfrom total 𝑃∗ and runoff, to present its effect being conflated into catchment\nproperties (n) (Eqn. 17). This method does not consider the potential effect of\nforestation-induced changes in land surface properties on runoff generation,\nimplicitly assuming that runoff generation depends only on precipitation\ncharacteristics. Therefore, the ratio of the remaining runoff (𝑅 −𝑃∗) to the\n2\nremaining precipitation (𝑃 −𝑃∗) in the forestation experiment is then assumed to be\n2\nidentical to that in the control simulation:\n𝑅\n2\n−𝑃∗ 𝑅\n2′\n𝑅\n1\n= = (17)\n𝑃 −𝑃∗ 𝑃 𝑃\n2 2′ 1\nwhere subscripts 1 and 2 denote the CTL and SCE simulations, respectively, and\nsubscript 2′ suggests that the effect of precipitation characteristics (𝑃∗) has been\nexcluded for the forestation scenario. Solving for 𝑃∗ yields:\n𝑅 ×𝑃 −𝑅 ×𝑃\n𝑃∗ = 2 1 1 2 (18)\n𝑃 −𝑅\n1 1\nFurther, the effects of changes in the remaining precipitation (𝑃 ), PET, and n on the\n2′\nremaining runoff (𝑅 ) are decomposed under the Budyko framework. This allowed us\n2′\nto identify the more realistic climate change effect (changes in the mean P, PET and\nprecipitation characteristics) and land surface effect on forestation-induced runoff\nchanges:\n1 𝜕𝐸𝑇 𝜕𝐸𝑇\n∆𝑅′ = − [( ) +( ) −2](𝑃 −𝑃 )\n𝑎𝑡𝑚 2 𝜕𝑃 𝜕𝑃 2′ 1\n1 2′ (19)\n1 𝜕𝐸𝑇 𝜕𝐸𝑇\n− [( ) +( ) ](𝑃𝐸𝑇 −𝑃𝐸𝑇 )+𝑃∗\n2 𝜕𝑃𝐸𝑇 𝜕𝑃𝐸𝑇 2 1\n1 2′\n1 𝜕𝐸𝑇 𝜕𝐸𝑇\n∆𝑅 = − [( ) +( ) ](𝑛 −𝑛 ) (20)\n𝑙𝑎𝑛𝑑 2 𝜕𝑛 𝜕𝑛 2′ 1\n1 2′\nTogether with the standard Budyko-based attribution, this extended approach, which\nexplicitly accounts for precipitation characteristics, provides a refined estimate for\nthe contributions of atmospheric feedbacks and land surface changes. Results from\nthis sensitivity test remain consistent with our main findings, confirming that forest-\natmosphere feedbacks predominantly drive the latitudinal contrast in runoff responses\nto global afforestation.” (Lines 779-808 in Methods section).\n“While the Budyko-based attribution highlights the dominant role of forest-\natmosphere feedbacks in driving runoff responses, it does not explicitly account for\nthe influence of altered precipitation characteristics, such as rainfall intensity and\nsnow fraction, on runoff generation processes69. To address this potential bias, we\nadopted a modified approach following Zhou et al.61, which isolates the effects of\naltered precipitation characteristics and incorporates these into atmospheric\nfeedbacks (Methods). The results from this modified method align well with our main\nfindings, confirming that forest-atmosphere feedbacks predominantly drive the\nlatitudinal divergence of forestation-induced runoff changes (Supplementary Fig. 8).”\n(Lines 245-253 in Results section).\nFig. R9 (also appears as Supplementary Fig. 8 in the revised version) The attribution\nof forestation-induced runoff changes based on the modified Budyko method with\nexplicit accounting for precipitation characteristics. (a) Spatial patterns of runoff\nchanges caused by forestation-induced alterations in precipitation characteristics. (b)\nSpatial patterns of runoff changes caused by forestation-induced alterations in mean\nprecipitation and PET changes. (c) Spatial patterns of runoff changes caused by\nforest-atmosphere feedbacks, representing the combined effects of mean P, PET and\nprecipitation characteristics (∆𝑅 ). (d) Spatial patterns of runoff changes caused by\n𝑎𝑡𝑚\nland surface changes (∆𝑅 ).\n𝑙𝑎𝑛𝑑\n[Reviewer #1 Minor Comment 1]\n8. Line 163: 80% seems a bit misleading in arid or semi-arid ecosystems considering\nthe . Maybe also mentioning the absolute values is a bit more transparent.\n[Response] Thank you for this suggestion. We have added the corresponding absolute\nvalues for clarity, as below: “Notably, in some arid and semi-arid regions, such as\nCentral Asia, the Sahel, South Africa, and Australia, although forestation causes\nsmall absolute runoff changes (<0.1 mm day-1), the relative changes can exceed 50%\ndue to the low background runoff (Supplementary Fig. 4a).” (Lines 166-169).\n[Reviewer #1 Minor Comment 2]\n9. Line 287: The average numbers are quite small (near-zero). Please show the\nstandard deviation (or the spread).\n[Response] Thank you for the comment. We have included the standard deviation to\nbetter represent the data variability, as follows: “Apart from precipitation changes,\nforestation also triggers asymmetric responses in PET, manifesting as a 0.7% (±\n0.6%) decrease in warm regions versus a 1.0% (± 1%) increase in cold regions (Fig.\n3e).” (Lines 341-343).\n[Reviewer #1 Minor Comment 3]\n10. Line 348 – 357: Seems like this is a summary of the results but not discussion.\n[Response] Thank you for pointing this out. We agree that this paragraph serves as a\nsummary rather than a discussion. Accordingly, we have changed the section title\nfrom ‘Discussion’ to ‘Conclusion and Discussion’ to avoid confusion.\n[Reviewer #1 Line Comments]\nLine 29: ‘Direct’ should be ‘direct’.\nLine 98: ‘forest-resulted’ should be ‘forest-induced’\nLine 176: ‘northern mid-to-high latitudes’ is a bit confusing. Do you mean high\nlatitudes in the northern hemisphere?\nLine 238: you mention PET-driven runoff decrease but the number in the parentheses\nis “+0.4%”. Do you mean “-0.4%”?\nLine 375: “...afforestation always reduces surface runoff” needs citation.\nFigure 1f: y-axis label is “PET/P - Dryness Index”. Maybe either dryness index or\nPET/P can be in parentheses as it now appears that you are subtracting the two terms.\nLine 580: “principal” should be “principle”\nLine 804: “Show” must be “Shown”?\nFigure 5: All occurrences of “Themodanymics” should be “Thermodynamics”.\n[Response]\nWe are grateful for your careful reading and detailed suggestions. All typographical\nand phrasing issues have been thoroughly checked and corrected as follows:\n• Line 29: “Direct” → “direct”.\n• Line 98: “forest-resulted” → “forest-induced”.\n• Line 176 (Line 181 in the new version): “northern mid-to-high latitudes” →\n“Northern-Hemisphere mid-to-high latitudes”.\n• Line 238 (Line 284 in the new version): The sign of the PET-driven runoff\nchange has been corrected.\n• Line 375 (Line 470 in the new version): Added a relevant citation to support\nthe statement that “afforestation always reduces surface runoff.”\n• Figure 1f: The y-axis label has been revised from “PET/P - Dryness Index” to\n“PET/P (Dryness Index)”.\n• Line 480 (Line 590 in the new version): “principal” → “principle”.\n• Line 804 (Line 1131 in the new version): “Show” → “Shown”.\n• Figure 5: All occurrences of “Themodanymics” have been corrected to\n“Thermodynamics”.\n[Reviewer #2 General Comment]\nThe manuscript by Fei Kan et al. addresses an important and timely topic: how global\nforestation influences runoff, with a particular focus on latitudinal divergence driven\nby forest-climate feedbacks. Using coupled land-atmosphere models and the Budyko\nframework, the authors find that forestation leads to increased runoff in tropical\nregions but reduced runoff in boreal regions. These findings underscore the\nimportance of incorporating hydrological considerations into afforestation strategies\naimed at climate mitigation. The manuscript is well-written and the technical details\nare carefully presented. However, I have several concerns regarding model\nuncertainty and methodological robustness that should be addressed before the\nmanuscript can be considered for publication in Nature Communications.\n[Response] We sincerely thank you for recognizing the timeliness of importance of\nthe topic and the perspective it adds to the existing literature. We also appreciate your\ninsightful comments on the model dependency and methodological robustness that\nhave guided us to strengthen our analyses and improve our MS. Below, we provide\ndetails on how we tackled the issues raised and how these have contributed to the\nrefinement of our MS. We believe that these revisions have greatly improved the\nquality of the MS.\n[Reviewer #2 Major Comment 1]\nThis study bears strong resemblance to previous work on deforestation, particularly in\nterms of methodology and key findings. While the authors conduct additional\nanalyses, the central conclusion—emphasizing the dominant role of forest-climate\nfeedbacks and regional divergence—is not entirely novel. Beyond these points, are\nthere any additional innovative insights that distinguish this study from prior\nliterature?\nMa, S., Zhou, S., Yu, B. & Song, J. Deforestation-induced runoff changes dominated\nby forest-climate feedbacks. Science Advances 10, eadp3964 (2024)\n[Response] We are grateful for this insightful comment and for highlighting the\nconnection to Ma et al.26 paper focused on deforestation. We are pleased to see this,\nbecause it underscores the significance of understanding the forest-runoff relationship.\nWe acknowledge that the two studies share a methodological foundation in\nquantifying forest-climate feedbacks, and both find their critical role in shaping the\nspatial structure of runoff responses to forest cover change. However, we believe this\nnew work advances current understanding of this topic, and are conceptually distinct,\nmechanistically richer, and directly relevant to future afforestation and climate–water\npolicy.\n1. A different and more policy-relevant scientific question\nWhile both studies focused on hydrological effects of forest change, the obvious\ndifference is that, Ma et al. focused on historical deforestation, whereas ours focus on\nfuture potential afforestation. Compared to the land use history (often localized to\ndeforested areas, our work tackles a forward-looking and policy-critical question\ncentral to global carbon neutrality and nature-based climate solutions. In particular, A\ncentral consideration in forestation policies is determining the optimal locations for\ntree planting to maximize climate benefits, our study provides a direct and concrete\nanswer to this from the perspective of hydrological outcomes. Our study highlights\nthat, while high-latitude afforestation has been questioned mainly for its albedo-\ninduced warming, our results add a hydrological caution, showing that such\nafforestation could also exacerbate regional water scarcity (detailed in Lines 453-\n465).\nFurthermore, the effects of are not simply mirror images: the hydrological responses\nto deforestation and afforestation can be highly nonlinear and spatially asymmetric,\nowing to differences in baseline climate, land-atmosphere coupling strength, and\nvegetation–radiation feedbacks. Importantly, regions identified as having potential\nfor afforestation are largely distinct from areas historically deforested — for\nexample, cropland or urban areas converted from forests are often unsuitable for\nreverting back to forests — meaning that their hydrological impacts have remained\npoorly constrained. In the revised manuscript, we further introduced three\nprogressively constrained forestation scenarios (detailed in Response to your\nComment 2), representing theoretical “maximum potential” to practically achievable\nforestation under current climate limits. This provides actionable insight for policy\ndecisions of climate mitigation and water resource management in large-scale\nafforestation planning, an aspect absent from previous deforestation-focused analyses.\nWe have highlighted the importance of investigating the hydrological impacts of\npotential afforestation in the revised Introduction, written as: “Most existing\nmodelling studies focus on the feedbacks of deforestation41,43,45,46. However, given that\nthe hydrological responses to afforestation and deforestation are asymmetric and that\nthe regions suitable for potential afforestation are largely distinct from historically\ndeforested areas44,47,48, it remains essential to investigate the hydrological\nconsequences of afforestation. In addition, these simulations predominantly rely on\nidealized (e.g., 100% or 50%) or historical afforestation/deforestation\nscenarios44,49,50. Such scenarios fail to account for current and local-specific climate\nconstraints on achievable forestation potential6,44,50, thus potentially limiting their\napplicability in guiding science-based strategies for future afforestation initiatives.”\n(Lines 85-94).\n2. Novel interpretation of the regional divergence\nOur study does not merely replicate regional patterns reported previously; it\nestablishes a mechanistic framework linking forestation-induced hydrological effects\nto thermal (cold vs. warm) and dryness (wet vs. dry) gradients. We reveal that\natmospheric feedback effects follow the thermal gradients, whereas land surface\neffects follow the dryness gradients. Specifically, atmospheric feedbacks dominate\nrunoff increases in warm, humid regions but suppress runoff in cold, high-latitude\nregions—revealing a predictable latitudinal structure of forest–climate coupling.\nMeanwhile, the land-surface (biophysical) effects show a unimodal relationship with\nthe dryness index (PET/P), with the strongest runoff suppression near the transitional\n(energy–water co-limited) zone. These organizing patterns have not been reported in\nprior studies and provide a unified theoretical basis for interpreting regional\nhydrological consequences of forest expansion. We have also emphasized this point in\nthe Discussion section, written as: “In contrast to the thermally/latitudinally\ndependent forest-atmosphere feedbacks, afforestation-induced land surface effect\ndrives spatially consistent runoff suppression along the dryness gradient, reaching\nmaximum intensity in transitional regions where the PET/P value approaches unity.”\n(Lines 413-416).\n3. Improved mechanistic understanding of the patterns\nBeyond quantifying total runoff responses, our analysis offers a deeper mechanistic\nunderstanding of the processes underlying the observed spatial divergence. For the\natmospheric feedback, we decomposed precipitation changes into thermodynamic\nversus dynamic components of the moisture budget, and further partitioned PET\nchanges into energy and humidity-driven terms. These analyses reveal that enhanced\ntropical precipitation primarily arises from strengthened large-scale circulation and\nmoisture convergence, whereas PET changes exhibit opposite latitudinal tendencies\ngoverned by the balance between available energy and atmospheric moisture demand.\nFor the land-surface effects, our integration of the Budyko framework provides a first-\nprinciples explanation for the nonlinear runoff response, showing why afforestation-\ninduced suppression peaks near the transitional PET/P ≈ 1 boundary. Together, these\nprocess-level insights move beyond the diagnostic scope of prior deforestation\nanalyses, offering a unified, physically interpretable framework for future forest–\nhydrology interactions under climate change. Associated discussion regarding this\npoint can be found in Lines 405-419.\n[Reviewer #2 Major Comment 2]\nThe authors adopt a “full-potential forestation” scenario. Is this scenario similarly\nidealized as those used in earlier studies? How realistic is it under current and future\nclimate and land-use constraints? Could comparable forestation pathways realistically\noccur in future scenarios? A critical issue is the reliability and accuracy of the\nsimulated runoff responses. To what extent have these runoff outputs been validated\nagainst observations? If observational constraints are limited, how confident can we\nbe in the attribution results derived from these model simulations?\n[Response] Thank you for raising these thought-provoking questions about the “full-\npotential forestation” scenario. Your questions and concerns primarily focus on two\nkey aspects: (1) the feasibility of our “full-potential forestation” scenario, and (2) the\nreliability of simulated runoff from the climate models.\n(1) Feasibility of the full-potential forestation scenario\nThe full-potential forestation (Bastin_Full) scenario used in this study represents the\ntheoretical maximum tree cover that current climatic conditions could support, as\ndefined in Bastin et al.1. Unlike previous studies that employed simplified “100%” or\n“50%” afforestation scenarios, Bastin_Full represents the upper-bound afforestation\npotential under current climate suitability. However, this scenario does not account for\nother limiting factors such as land-use constraints, socio-economic feasibility, shifts in\nfuture climate regimes, and land-climate feedbacks (e.g., albedo effects). As such,\nafforestation to this full potential is unlikely to be realized in practice. Our goal in\nusing this idealized scenario is not to predict future land cover but to provide a\ntheoretical framework for assessing the potential hydrological effects of forestation\nunder a hypothetical upper-bound scenario.\nTo address this limitation and evaluate the robustness of our results, we additionally\ndesigned two sensitivity scenarios: Bastin_Refined_Climate and\nBastin_Refined_Climate&Land. Specifically, Bastin_Refined_Climate, is based upon\nBastin_Full but excludes areas with frequent fire and regions with additional climate\nconstraints due to forest-induced albedo changes. This scenario addresses two major\ncritiques of the Bastin’s data: the overestimation of tree cover potential in grassland\nand savanna ecosystems with frequent fire, and the modeling of new tree cover in\nwater-limited regions where trees reduce albedo and induce net climate warming2–4.\nApplying these constraints lead to a lower estimated afforestation potential,\nparticularly in tropical grassland and savanna ecosystems across Africa (Fig. R10b).\nBastin_Refined_Climate&Land, is an extended version of Bastin_RefinedClimate that\nadditionally excludes croplands, built-up lands and wetlands. This represents the most\nfeasible forestation scenario. We excluded urban and agricultural areas due to their\ncritical importance for human sustenance and socio-economic activities. We applied\nthe wetland precautionary exclusions to protect large soil organic carbon (SOC) \nstocks that may be vulnerable to losses following the establishment of trees5,6.\nCompared with the Bastin_Refined_Climate scenario, the further reduction in\nafforestation area in Bastin_Refined_Climate&Land predominantly occurs in\ncropland-dominated regions like Europe, African croplands, Southeast Asia, and\neastern South America (Fig. R10c). Specific details on the sensitivity scenario settings\nand the exclusionary datasets used can be found in the updated Methods section,\nwritten as:\n“Two sensitivity experiments with constrained forestation\nAs previously noted, the Bastin et al.’s data likely overestimate potential tree cover;\nthus, our full-potential forestation scenario (Bastin_Full) represents the upper bound\nof global forestation potential. In this regard, we designed two additional scenarios\n(Bastin_Refined_Climate and Bastin_Refined_Climate&Land) by progressively\nintroducing more realistic constraints to test the robustness of our results. These three\nscenarios represent a range of possible forestation pathways, from the theoretical\nmaximum to the most practical feasible.\nBastin_Refined_Climate builds upon Bastin_Full by excluding regions with frequent\nfires and where forestation would produce net warming due to reduced surface\nalbedo. This refinement addresses two major critiques of the Bastin et al.’s dataset:\noverestimated potential in fire-prone grassland and savanna ecosystems, and the\nneglect of forestation-caused regional warming due to reduced albedo65,66. Following\nFesenmyer et al.64, we used the 500 m MODIS Burned Area dataset to remove all non-\ncropland areas experiencing two or more fires during 2002–2022, assuming that such\nfrequencies substantially limit tree density114. We further excluded regions where\nalbedo-induced radiative warming exceeds the cooling from carbon sequestration of\npotential forestation, based on the dataset of Halser et al.115. These constraints\nmarkedly reduce the estimated afforestation potential in tropical grassland and\nsavanna ecosystems, particularly across Africa (Supplementary Fig. 1b).\nBastin_Refined_Climate&Land incorporated additional constraints on\nBastin_Refined_Climate by excluding croplands, built-up areas, and wetlands to\nensure land-use sustainability and feasibility. Urban and agricultural areas, given\ntheir essential roles in food production and human livelihoods, were masked out using\nthe ESA CCI Global Land Cover dataset for 2000. Wetlands were excluded using the\nGlobal Lakes and Wetlands Database (GLWD, version 2) to prevent potential losses\nof large soil organic carbon (SOC) stocks following afforestation. Compared with the\nBastin_Refined_Climate scenario, the further reduction in forestation potential in\nBastin_Refined_Climate&Land predominantly occurs in cropland-dominated regions\nlike Europe, African croplands, Southeast Asia, and eastern South America\n(Supplementary Fig. 1c).” (Lines 627-657).\nBoth further constrained scenarios (Bastin_Refined_Climate and\nBastin_Refined_Climate&Land) exhibit a latitudinal pattern of runoff responses\nsimilar to that of the Bastin_Full scenario, with increases in the tropics and reductions\nat high latitudes (Fig. R11). Using the Budyko framework to attribute forestation-\ninduced runoff changes, we find that the dominant role of forest-atmosphere\nfeedbacks in shaping the latitudinal gradient of runoff responses remains robust across\ndifferent afforestation scenarios (Fig. R12). While the overall pattern remains\nconsistent, discrepancies are present in regional responses among the scenarios. In\nBastin_Refined_Climate, reduced forestation area over tropical grasslands and\nsavannas leads to less strong tropical runoff increases. In\nBastin_Refined_Climate&Land, the further exclusion of cropland and urban areas\nweakens, but not reverses, the decrease runoff in some high-latitude regions. We have\nincorporated the new results from these additional sensitivity tests into the revised\nmanuscript, as follows:\n“Since the full-potential afforestation scenario using the Bastin et al.’s dataset may\noverestimate the actual reforestation potential64–66, we further incorporated additional\nconstraints on the potential forestation maps to test the robustness of results. Building\non Bastin_Full, a new scenario (Bastin_Refined_Climate) additionally excludes areas\nwith frequent fire occurrences or with expected albedo-induced regional warming\nafter afforestation, and another new scenario (Bastin_Refined_Climate&Land)\nfurther excludes croplands, built-up lands, and wetlands. Our simulations forced by\nrefined potential forestation maps produced a latitudinal gradient of runoff responses\nsimilar to that of Bastin_Full, though with regional difference in the hydrological\neffects (Supplementary Fig. 5). Compared with Bastin_Full, reduced forestation area\nover tropical grasslands and savannas leads to less strong tropical runoff increase;\nand further exclusion of afforestation over croplands and urban areas in\nBastin_Refined_Climate&Land show less significant decreases in high-latitude\nregions and weaker increases in low-latitude regions. (Supplementary Figs. 1 and 5).\nIn both afforestation scenarios, the contrasting pattern of enhanced tropical runoff\nand suppressed high-latitude runoff remains evident.” (Lines 189-203);\n“Furthermore, the two further constrained forestation scenarios consistently show\nthat forest-atmosphere feedbacks strongly shape the latitudinal gradient of runoff\nresponses (Supplementary Fig. 7).” (Lines 241-243).\nFig. R10 (also appears as Supplementary Fig. 1 in the revised version) Spatial pattern\nof tree fraction changes (∆𝑇𝑟𝑒𝑒 𝑐𝑜𝑣𝑒𝑟) due to global forestation under a hierarchy of\nprogressively constrained scenarios: (a) Bastin_Full scenario, (b)\nBastin_Refined_Climate scenario, (c) Bastin_Refined_Climate&Land scenario.\nFig. R11 (also appears as Supplementary Fig. 5 in the revised version) Forestation-\ninduced runoff changes (ΔR) across the two additionally constrained forestation\nscenarios. (a) Bastin_Refined_Climate scenario, (b) Bastin_Refined_Climate&Land\nscenario.\nFig. R12 (also appears as Supplementary Fig. 7 in the revised version) Runoff\nchanges caused by indirect forest-atmosphere feedbacks and direct land surface effects of\nglobal potential forestation across the two additionally constrained forestation\nscenarios. (a, b) Spatial patterns of runoff changes associated with forest-\natmosphere feedbacks (∆𝑅 ) in Bastin_Refined_Climate scenario (a) and\n𝑎𝑡𝑚\nBastin_Refined_Climate&Land scenario (b). (c, d) The same with (a, b) but for land\nsurface effects (∆𝑅 ).\n𝑙𝑎𝑛𝑑\n(2) Reliability of simulated runoff\nWe conducted a validation of the simulated runoff against observed streamflow data\nderived from the Global Streamflow Indices and Metadata Archive (GSIM)11,12. In the\nSupplementary Texts, we provide details on this streamflow database and describe the\ndata screening criteria and processing, as also copied here:\n“We conducted a validation of the simulated runoff using streamflow observations\nfrom the Global Streamflow Indices and Metadata Archive (GSIM)1,2, which provides\ndaily records of streamflow from over 30,000 stations worldwide. To ensure\ncomparability with model’s coarse spatial resolution and its lack of representation of\ndirect human interventions (e.g., dam construction, irrigation), we screened the\navailable catchments based the following criteria: (1) with a drainage area greater\nthan 20,000 km2; (2) with at least 15 years of complete annual streamflow records\nbetween 1971 and 2010; (3) with ‘high’ or ‘medium’ boundary quality, indicating less\nthan 10% difference between the calculated and reported drainage area; (4) with\nirrigation areas < 1% of total area or reservoir capacity < 10% of mean annual\nstreamflow; (5) Only catchments for which four homogeneity tests (i.e., the standard\nnormal homogeneity test3, the Buishand range test4, the Pettitt test5, and the von\nNeumann ratio test6, adopted by the GSIM data) all fail to reject the null hypothesis of\nno break-point in the time series at the 1% significance level were selected. The\ncatchmental irrigation area and reservoir capacity, and quality control for catchment\nboundaries, are all derived from the GSIM metadata.\nApplying these criteria led to a total of 270 catchments. The locations of these\ncatchments and the number of valid annual observations are shown in the\nSupplementary Fig. 26a. For comparability with models’ runoff output, mean annual\nrunoff (in mm day-1) from observations were obtained by dividing the river discharge\n(m3 day-1) by the station’s upstream catchment area (km2). For the simulated runoff by\nIPSL and CESM (based on the CTL run), we first interpolated model output to a 1°\nresolution. For each selected catchment, the catchment-scale runoff was calculated as\nthe areal-weighted average of grid cells falling within the catchment.”\n(Supplementary Text 3).\nUsing streamflow observations from the 270 selected catchments (as shown in Fig.\nR13a), we evaluated the performance of modelled runoff. As expected, IPSL-\nsimulated runoff exhibits notable deviations from observed values especially in large\ncatchments (Fig. R13b). This is because IPSL does not account for the effects of\nurbanization, anthropogenic land-use changes, and river routing processes within the\ncatchments particularly those large ones. Despite this difference for specific\ncatchments, we find an overall significant positive correlation (r = 0.66, p < 0.01)\nbetween the IPSL simulations and observations (Fig. 13b), indicating that IPSL\nsuccessfully captures characteristics of runoff in ‘wet’ versus ‘dry’ catchments.\nImportantly, across all available catchments, the median difference between IPSL\nestimates and observations is significantly indistinguishable from zero (Fig. 13d),\nsuggesting no systemic bias in IPSL simulations of runoff at the continental scale.\nFurthermore, we find that IPSL outperforms CESM in runoff simulations. Evaluation\nof CESM-based estimates show a weak, though significant, correlation (r) of 0.20 (p\n< 0.01) (Fig. R13c), and a significant negative deviation of the modelled runoff from\nobservations, indicating a systemic underestimation of runoff (Fig. R13d). Given this\napparent discrepancy, we chose not to present the CESM-based estimates in the main\n main\nfigures, but instead include this as an additional model test to verify our primary\nfindings based on IPSL. In the revised manuscript, we presented the evaluation of\nmodel performance based on streamflow observations. The added text reads as\nfollows:\n“Model performance was further evaluated using observed streamflow records of 270\nscreened catchments from the Global Streamflow Indices and Metadata Archive\n(GSIM)109,110 (Supplementary Text 3). The IPSL-CM successfully reproduces cross-\ncatchment runoff variability (r = 0.66, p < 0.01), with the median of model-\nobservation difference statistically indistinguishable from zero. This indicates no\nsystematic bias in the IPSL-modelled runoff, supporting the model capability for\nassessing afforestation-induced runoff responses (Supplementary Fig. 26).” (Lines\n558-564 in revised manuscript);\n“It is worth noting that while CESM-simulated runoff is significantly correlated with\nobservations, CESM overall underperforms IPSL (Supplementary Fig. 26). We\ntherefore present the CESM-based results in the Supplementary Information to\nsupport the robustness of our primary findings from IPSL.” (Lines 621-625 in revised\nmanuscript).\nFig. R13 (also appears as Supplementary Fig. 26 in the revised version) Evaluation of\nthe model-simulated mean annual runoff across the 270 catchments. (a) Spatial\ndistribution of the 270 selected catchments and the number of years with available\nobservations per catchment. (b, c) Scatter plots of annual mean runoff simulated by\nIPSL (b) and CESM (c) versus corresponding observations across the 270 catchments.\nThe size of the markers represents the catchment area, and the color of the markers\ndenotes the number of available observation years for each catchment. (d) Box plots\nof the difference between modelled and observed annual mean runoff, showing the\nmedian (horizontal lines), 25th, 75th quartiles, maximum and minimum values (top\nand bottom of the whiskers), outliers (dots) and the 95% confidence interval for the\nmedian (notches).\n[Reviewer #2 Major Comment 3]\nThe study primarily relies on IPSL-CM, with some additional analyses using CESM,\nto simulate forestation effects. Dependence on one or two models raises concerns\nabout the robustness of the findings, especially given the relatively coarse spatial\nresolution of IPSL-CM. Prior studies have demonstrated substantial inter-model\nvariability in forestation-induced changes in the water cycle: some models emphasize\nmoisture recycling, while others highlight atmospheric circulation adjustments. Even\nwithin CMIP6 LUMIP experiments, large discrepancies exist among models under\ncomparable scenarios. I recommend integrating CESM results more prominently into\nthe main text and discussing model uncertainty in greater detail. It is difficult to place\nconfidence in results derived primarily from a single model.\nDe Hertog SJ, et al. Effects of idealized land cover and land management changes on\nthe atmospheric water cycle. Earth Syst Dynam 15, 265-291 (2024)\nPortmann, R. et al. Global forestation and deforestation affect remote climate via\nadjusted atmosphere and ocean circulation. Nat Commun 13, 5569 (2022).\nLawrence DM, et al. The Land Use Model Intercomparison Project (LUMIP)\ncontribution to CMIP6: rationale and experimental design. Geosci Model Dev 9,\n2973-2998 (2016).\n[Response] We appreciate your valuable comment concerning the model dependency.\nWe understand your concern that relying on a single model may limit the robustness\nof the conclusions, and this is indeed the motivation for us to include additional\nsimulations using CESM. The broadly consistent hydrological responses produced by\nthe two models, as well as our further verification with observation-based evidence\n(see detailed response to your Major Comment 10), enhance our confidence in the\nmain findings. In our original manuscript, as you also note, we presented the CESM\nresults in the Supplementary Figures to complement the IPSL-CM results. In this\nversion, we retained this organization because our new evaluation against runoff\nobservations (see our response to your Major Comment 2) showed that IPSL notably\noutperforms CESM in capturing the inter-catchment runoff variability the magnitudes\nof observed runoff. Therefore, we prioritized IPSL-CM in the main text to better\nrepresent the observationally constrained hydrological responses, while retaining\nCESM results as complementary evidence of model robustness. Furthermore, while\nCESM show broadly consistent results with IPSL, we detected nuanced regional\ndifferences. In the revised manuscript, we have included a comparative discussion\nabout the IPSL and CESM results, highlighting the key differences between the two\nstructurally different models, written as: “Although CESM overall supports the IPSL-\nbased results, some regional discrepancies are notable due to structural and physical\ndifferences between the two models. For example, in Southeast Asia, precipitation\nincreases exceed ET increases in IPSL, whereas the opposite pattern is simulated in\nCESM (Fig.1 versus Supplementary Fig. 20). Our moisture budget decomposition\nindicates that this difference stems from contrasting atmospheric circulation\nresponses: IPSL simulates an intensification of convective precipitation, while CESM\nsuggests a suppression (Supplementary Fig. 14c versus Supplementary Fig. 23c).”\n(Lines 432-439).\nIn light of your comment, we have also compared our findings with previous model-\nbased assessments of global forestation impacts on the water cycle. While few studies\nhave explicitly examined runoff responses, several have analyzed precipitation\nchanges following idealized forestation28–31. All these model-based studies\nconsistently show a clear latitudinal gradient, with precipitation generally increasing\nin the tropics and decreasing or showing weaker changes at higher latitudes. These\npatterns are in line with our findings, despite regional discrepancies. For example, De\nHertog et al. (2024)31 and Portmann et al. (2022)28 report precipitation responses that\ndiffer regionally from ours, which is unsurprising given their different experimental\ndesigns (50% and 100% idealized afforestation, respectively) compared with our\nclimate-constrained potential afforestation scenario. Importantly, given that the\nhydrological responses to afforestation and deforestation are generally opposite in\nsign, the ‘deforest-globe’ scenario of the Coupled Model Intercomparison Project\nPhase 6 (CMIP6) Land Use Model Intercomparison Project (LUMIP) experiments —\nprovide multi-model evidence supporting our findings. For example, Ma et al. (2022)\nhighlighted the dominance of the atmospheric feedbacks induced by idealized global\ndeforestation, which exerted a similar latitudinal contrast in the response of runoff,\nwith reductions in the tropics and increases at mid-to-high latitudes. This\ndeforestation-driven pattern indirectly confirmed the latitudinal gradient of runoff\nresponse identified in our study. We have incorporated these model-based discussions\ninto the revised manuscript, which now reads: “Our results are also corroborated by\nprevious model-based assessments of global forestation effects on the hydrological\ncycle. For example, De Hertog et al.49 and Portmann et al.44 consistently show a clear\nlatitudinal gradient, with precipitation generally increasing in the tropics and\ndecreasing or showing weaker changes at higher latitudes, in line with our findings”\n(Lines 212-216);\n“The dominant role of forest-atmosphere feedbacks in shaping runoff responses is\nfurther supported by an ensemble of idealized global deforestation simulations from\nthe Coupled Model Intercomparison Project Phase 6 (CMIP6) Land Use Model\nIntercomparison Project (LUMIP), in which runoff responses exhibit a similar\nlatitudinal gradient of tropical reductions and mid-to-high latitude increases45. Given\nthat hydrological responses to afforestation and deforestation are generally opposite\nin sign, this opposite pattern under deforestation indirectly corroborates the\nrobustness of our findings.” (Lines 439-446).\n[Reviewer #2 Major Comment 4]\nLines 37–47: This section emphasizes carbon benefits, whereas the focus of this study\nis on the hydrological cycle. Please consider rebalancing the discussion to align with\nthe core research objective.\nThe Budyko attribution approach is applied, yet the introduction lacks any description\nor justification of this method.\nSeveral statements in the introduction are not original contributions and should be\nsupported with appropriate citations.\nThe introduction discusses forestation-runoff relationships extensively but provides\nlimited context on latitudinal divergence, which is central to this study.\n[Response] These are very useful comments that guide use to improve the focus and\nclarity of our Introduction. Below we provide details about the revisions we made\naccording to each point. Collectively, these revisions enhance the clarity and\nreadability of the Introduction and reinforce its alignment with the study’s main\nfocus.\n• Regarding the need to rebalance carbon and water: We agree that our original\nversion excessively emphasized on the carbon benefits of forestation. In the\nrevised Introduction, we have condensed this part and shifted the focus toward\nhydrological impacts to better align with the core objective of this study, which\nnow read: “Forestation — including afforestation, reforestation and forest\nmanagement — has been advocated as a cost-effective nature-based strategy to\ncurb climate change by sequestering atmospheric CO (refs1–5). To optimize the\n2\nclimate benefits of forestation, strategic tree planting in suitable locations is\nessential. Previous studies indicate that, when moving from tropical to boreal\nregions, not only does the carbon sequestration potential of forests decline, but\nthe biophysical effects also shift from cooling to warming mainly due to a\ndarkening of tree canopies, cautioning the limited climate benefits of afforestation\nin boreal regions6–10.” (Lines 36-42).\n• Regarding the Budyko framework: Following your suggestion, and to provide\nreaders with necessary methodological context, we have now added a brief\ndescription and justification of the Budyko attribution approach, which now read:\n“The runoff-suppressing effect of afforestation was also supported by analyses\nbased on the Budyko framework, which also offers an empirical approach for\nattributing observed runoff changes into contributions from local atmospheric\nchanges (i.e., altered precipitation and potential evapotranspiration, PET) and\nvegetation cover changes26,27” (Lines 62-66).\n• Regarding the need for additional citations: We have carefully reviewed the\nIntroduction and added appropriate references to support several background\nstatements, ensuring that all non-original claims are now properly cited. For\nexample, we have included citations at the end of the sentence “In addition, these\nsimulations predominantly rely on idealized (e.g., 100% or 50%) or historical\nafforestation/deforestation scenarios44,49,50” to acknowledge the relevant previous\nmodeling studies. Please refer to the new Introduction for all changes to citations.\n• Regarding the discussion of latitudinal divergence: Following your advice, we\nhave clarified the scientific context and significance of our focus on the latitudinal\ndivergence in forestation-runoff relationships, which now reads: “Although the\ninfluence of PET changes is often considered secondary to that of precipitation, it\ncan become more pronounced in energy-limited high-latitude regions45. This is\nbecause the transition from snow-covered surfaces to darker forests substantially\nincreases surface net radiation10,51, which enhances PET and thus suppresses\nrunoff generation. Conversely, in tropical regions where the atmosphere is more\nconvectively unstable and oceanic moisture supply is abundant52–54, high rates of\nmoisture recycling over rainforests amplify precipitation responses to forest\nexpansion36,55. For example, previous modeling studies have suggested that large-\nscale afforestation enhances precipitation more substantially in the tropics than in\nboreal regions44,49. These coexisting and counteracting mechanisms imply the\nbalance between effects of precipitation and PET may vary geographically. So\nfar, it remains unknown about the net effect of forest-atmosphere feedbacks on\nrunoff, and the overall hydrological impact that further incorporates the direct\nforest-driven ET increase.” (Lines 99-111).\n[Reviewer #2 Major Comment 5]\nThe results section describes percentage changes, but some figures appear to display\nabsolute numerical changes. Please clarify this inconsistency.\n[Response] We appreciate this useful comment. In our analyses based on the water-\nbalance and Budyko attribution frameworks, we used absolute numerical changes to\nretain the physical meaning and comparability among different water‐balance\ncomponents (e.g., to assess the rate of runoff change relative to precipitation).\nIn contrast, in the Results section, we expressed the results as percentage changes when\ncomparing the hydrological responses between cold and warm regions. This is\nbecause the magnitude of mean runoff differs substantially between these regions\n(with generally larger runoff in warm areas). Using relative changes allows for a more\nmeaningful comparison of forest–atmosphere feedbacks across the two climate zones.\nWe have clarified this inconsistency in the revised manuscript to avoid confusion and\nenhance readability, written as: “Given that mean runoff differs by several-fold\nbetween warm and cold regions, we used relative rather than absolute changes in our\nsubsequent analysis to facilitate comparison of forestation-induced hydrological\nresponses.” (Lines 267-269).\n[Reviewer #2 Major Comment 6]\nFigure 1 reveals several unexpected findings that warrant more careful discussion. For\nexample, precipitation changes exceed evapotranspiration changes in magnitude. This\nsuggests that large-scale atmospheric circulation shifts and cascading moisture\nrecycling effects may play a substantial role. In Southeast Asia, minor changes in\nevapotranspiration appear to trigger pronounced precipitation increases. However,\nthese phenomena are not reproduced by CESM, highlighting the model dependence of\nthe results. Supplementary Figure 4 statistics should be incorporated into the main\ntext to strengthen the argument. Does CESM support the key findings of IPSL-CM\nsimulations? The results for warm regions appear to differ considerably. The results\nfor China in Figure 1d and Supplementary Figure 4d seem inconsistent with prior\nstudies. Have the authors compared their findings with recent global forestation-water\ncycle simulations? There appear to be notable discrepancies.\nLi, Y. et al.. Sci. Adv. 4, eaar4182 (2018) Divergent hydrological response to large-\nscale afforestation and vegetation greening in China\nKing JA, Weber J, Lawrence P, Roe S, Swann ALS, Val Martin M. Global and\nregional hydrological impacts of global forest expansion. Biogeosciences 21, 3883-\n3902 (2024).\n[Response] Thank you very much for your careful review and for these insightful\ndetailed comments regarding the robustness of our model-based results. We see you\nraised several important points concerning (1) the larger precipitation response\ncompared to evapotranspiration (ET), (2) the comparison between IPSL-CM and\nCESM, and (3) consistency with previous studies. Our detailed responses to each of\nthem are provided below.\n(1) On the larger precipitation increase relative to evapotranspiration\nYes, our model simulations suggests that precipitation increases more strongly than\nET, and this is a robust signal as both trends are statistically significant. This indicates\nthat global forestation alters precipitation not only through direct or cascading\nrecycling of evaporated water from new forests, but also by modifying large-scale\natmospheric circulation and boosting atmospheric moisture convergence. Our analysis\nindicate that forestation-driven circulation changes enhance both horizontal and\nvertical moisture transport in tropical monsoon regions (Fig. R14), allowing more\noceanic moisture to be advected inland. Thus, in monsoon regions where precipitation\nprimarily originates from oceanic sources, forestation strengthens the transport of\nmoisture from the ocean to land. This is supported by previous studies showing that\nvegetation changes in monsoon regions can affect rainfall by perturbing sea-breeze\ncirculations32–38. We further compared our results with studies that quantified the\nrecycling of new forests-sourced moisture (ET) using moisture-tracking models.\nThose studies, without accounting for atmospheric moisture convergence, suggest that\nET increases exceed precipitation increases39,40. This finding, when compared with\nour fully coupled simulations, revealed the essential role of atmospheric moisture\nconvergence, in determining the balance between precipitation and ET changes. We\nhave discussed the role of large-scale atmospheric circulation shifts in the\nhydrological response to afforestation in in Lines 486–498 of the revised manuscript.\nFig. R14 (also appears as Supplementary Fig. 14 in the revised version) Moisture\nbudget decomposition of IPSL-simulated precipitation changes. Spatial patterns of\nglobal forestation-induced precipitation changes associated with different atmospheric\nprocesses, including thermodynamic effect (ΔWV, a), modified vertical moisture\nadvection (ΔVert, b), altered horizontal moisture advection (ΔAdv, c), and land\nsurface ET changes (ΔET, d). Res (e) represents the precipitation changes that cannot\nbe explained by the four additive processes. Black stipples denote statistically\nsignificant change at the 5% significance level using the Student’s t-test.\n(2) On the comparison between IPSL-CM and CESM\nTo evaluate whether CESM supports the key findings of IPSL-CM, we also applied\nthe Budyko framework to decompose the CESM-simulated runoff changes into the\ncontributions of atmospheric feedbacks and land surface changes (Fig. R15). The\nCESM results also indicate that forest-atmosphere feedbacks primarily shape the\nspatial pattern of runoff responses to forestation. To understand the role of forest-\natmosphere feedbacks, we further performed the water vapor budget analysis for\nprecipitation changes and the attribution of PET changes using net radiation and\natmospheric moisture demand. This additional analysis confirms that CESM supports\nthe main results from IPSL (Fig. R16). For instance, CESM also suggests that the\npronounced precipitation increases in tropical regions primarily arise from enhanced\nhorizontal and vertical moisture transport associated with circulation changes.\nWhile CESM overall supports the main conclusions derived from IPSL, some\nregional discrepancies are expected given the structural and physical differences\nbetween the two models. For example, in Southeast Asia (as you mentioned),\nprecipitation increases exceed ET increases in IPSL, whereas the opposite occurs in\nCESM. Our moisture budget decomposition indicates that this difference stems from\ncontrasting atmospheric circulation responses: IPSL simulates stronger convective\nprecipitation, while CESM shows a reduction in convective rainfall (comparing Fig.\nR14 and Fig. R17). We chose not to include CESM results in the main text because\nCESM exhibits lower reliability in reproducing observed runoff variations (see our\nresponse to Comment 2). However, in line with the reviewer’s suggestion, we now\nprovide a more detailed discussion of the differences between IPSL and CESM in the\nrevised manuscript, which now read:\n“To assess the robustness of our model-based findings, we also performed equilibrium\nexperiments using an independent model, Community Earth System Model (CESM).\nThis model reproduces a similar latitudinal (thermal) pattern of forestation-induced\nrunoff changes, with increases in warm regions and decreases in cold regions, albeit\nwith slightly different magnitudes (Supplementary Fig. 20). Consistent with IPSL-CM,\nCESM indicates that forest-atmosphere feedbacks predominantly shape the latitudinal\ngradient of runoff responses (Supplementary Fig. 21). Furthermore, both the water\nvapor budget analysis for precipitation changes and the attribution of PET changes\nusing net radiation and atmospheric moisture demand corroborate the main\nconclusions derived from IPSL-CM (Supplementary Fig. 22). For instance, regarding\nprecipitation responses to afforestation, CESM confirms that the pronounced\nincreases in warm regions primarily result from enhanced horizontal and vertical\nmoisture transport driven by circulation adjustments (Supplementary Fig. 22d).\nAlthough CESM overall supports the IPSL-based results, some regional discrepancies\nare notable due to structural and physical differences between the two models. For\nexample, in Southeast Asia, precipitation increases exceed ET increases in IPSL,\nwhereas the opposite pattern is simulated in CESM (Fig.1 versus Supplementary Fig.\n20). Our moisture budget decomposition indicates that this difference stems from\ncontrasting atmospheric circulation responses: IPSL simulates an intensification of\nconvective precipitation, while CESM suggests a suppression (Supplementary Fig.\n14c versus Supplementary Fig. 23c).” (Lines 421-439).\nFig. R15 (also appears as Supplementary Fig. 21 in the revised version) Runoff\nchanges caused by indirect forest-atmosphere feedbacks and direct land surface\neffects of global potential forestation in CESM simulations. (a-d) Geographical\npatterns of runoff changes associated with forest-atmosphere feedbacks (∆𝑅 ; a)\n𝑎𝑡𝑚\nand land surface changes (∆𝑅 ; c) respectively. Shown are the mean results derived\n𝑙𝑎𝑛𝑑\nfrom using the two PET algorithms. (b, d) Distribution of ∆𝑅 (e) and ∆𝑅 (f)\n𝑎𝑡𝑚 𝑙𝑎𝑛𝑑\nin the space of annual mean temperature and dryness index (PET/P) in CTL. Marginal\ncurves indicate the partial dependence of the ∆𝑅 or ∆𝑅 on temperature (top)\n𝑎𝑡𝑚 𝑙𝑎𝑛𝑑\nand PET/P (right). The PET value is the mean results of the two PET algorithms.\nFig. R16 (also appears as Supplementary Fig. 22 in the revised version) Regional\nmechanisms underlying the latitudinal divergence in runoff responses due to forest-\natmosphere feedbacks of forestation based on CESM simulations. (a) Regional\ncomponents of precipitation relative change associated with different processes in\nboth warm and cold regions. The four main processes are thermodynamic effect\n(ΔWV), altered horizontal advection (ΔAdv), altered vertical transport (ΔVert) and\nland ET changes (ΔET). The differences between precipitation changes (ΔP) and the\ncombined contribution from the four processes are residual (Res), representing the\nimpacts of transient eddies. (b) Regional annual mean of forestation-induced relative\nchanges in PET (ΔPET), surface available energy (Δ(R -G)), atmospheric vapour\nn\npressure deficit (ΔVPD), respectively. The bar plots of ΔPET are the mean results\nderived from using the two PET algorithms. Estimates by Penman-OW and PM-RC in\n(b) are shown as circles and triangles, respectively. Error bas in (a) and (b) indicate\nthe 1-standard deviation of the last 60-year series. ** p < 0.05; * p < 0.1.\nFig. R17 (also appears as Supplementary Fig. 23 in the revised version) The\ndecomposition of CESM-simulated precipitation change based on moisture budget.\nSpatial patterns of global forestation-induced precipitation change associated with\ndifferent processes, including thermodynamic effect (ΔWV, a), modified vertical\nmoisture advection (ΔVert, b), altered horizontal moisture advection (ΔAdv, c), land\nsurface ET changes (ΔET, d), and other processes (Res, e). Res represents the\nprecipitation change that cannot be explained by the four additive processes. Black\nstipples denote statistically significant change at the 5% significance level using the\nStudent’s t-test.\n(3) On the comparison with previous studies\nWe also compared our findings with previous global and regional afforestation-\nhydrology simulations. Caution is needed for interpreting discrepancies across studies\nas they may arise from different experimental design and temporal focus. Our\nexperimental setup represents equilibrium-state responses to idealized 100%\nafforestation28,30,31 constrained by present-day climate, whereas many previous studies\nexamined transient or historical reforestation trajectories41. For instance, Li et al.41\nexamined transient hydrological responses to observed afforestation in China, while\nKing et al.29 explored transient climate feedbacks of afforestation under SSP1-2.6.\nSuch differences in timescale and forcing can introduce difference to the simulated\npatterns.\nYour observation that both IPSL and CESM show larger increases in water yield over\nChina compared to Li et al.41 can be explained by differences in spatial aggregation.\nLi et al. (2023) reported near-zero national mean change because opposing local\neffects—precipitation increases in southeastern China and decreases in northern\nregions—offset each other. When examined regionally, their results also show that\nprecipitation increases exceed ET increases in the humid southeast, which aligns\nclosely with our findings. Moreover, our conclusions are further supported by\nmultiple independent model lines of evidence. Other idealized GCM experiments28,31,\nand high-resolution regional climate simulations35, consistently indicate increased P–\nET over China, further supporting our conclusions. In the revised manuscript, we\nhave discussed our results with several other modeling studies to reinforce the\nrobustness of our conclusions, as detailed below:\n“Our results are also corroborated by previous model-based assessments of global\nforestation effects on the hydrological cycle. For example, De Hertog et al.49 and\nPortmann et al.44 consistently show a clear latitudinal gradient, with precipitation\ngenerally increasing in the tropics and decreasing or showing weaker changes at\nhigher latitudes, in line with our findings” (Lines 212-216);\n“The dominant role of forest-atmosphere feedbacks in shaping runoff responses is\nfurther supported by an ensemble of idealized global deforestation simulations from\nthe Coupled Model Intercomparison Project Phase 6 (CMIP6) Land Use Model\nIntercomparison Project (LUMIP), in which runoff responses exhibit a similar\nlatitudinal gradient of tropical reductions and mid-to-high latitude increases45. Given\nthat hydrological responses to afforestation and deforestation are generally opposite\nin sign, this opposite pattern under deforestation indirectly corroborates the\nrobustness of our findings.” (Lines 439-446).\n“For example, previous modeling studies based on a regional climate model (40-km\nresolution)90 or a zoomed IPSL-CM have suggested enhanced runoff in response to\nhistorical afforestation in China40, consistent with our GCM-based results.” (Lines\n528-531).\n[Reviewer #2 Major Comment 7]\nThe use of the Penman-Monteith model for PET calculation could introduce potential\nbiases. Recent studies suggest that the Penman equation overestimates PET because\nthe assumption of a saturated surface is not satisfied over most land surface. I\nrecommend considering the energy balance approach (PETe) instead.\nZhou, S. & Yu, B. Reconciling the Discrepancy in Projected Global Dryland\nExpansion in a Warming World. Glob Change Biol, 31: e70102 (2025).\nZhou, S., & Yu, B. Physical basis of the potential evapotranspiration and its\nestimation over land. Journal of Hydrology, 641, 131825 (2024).\n[Response] Thank you a lot for this thoughtful comment about the potential\noverestimation of PET when using the Penman-Monteith model, and thank you for\nbringing these useful papers into our attention. Following your suggestion, we\nadditionally calculated PET using the energy balance approach (PETe) and compared\nit with the two Penman-based variants (Penman-OW and PM-RC), referred to as\nPETow and PETrc, respectively. We indeed found that both PETow and PETrc\noverestimate PET, particularly in arid regions (Fig. R18). This finding is consistent\nwith Zhou et al.42,43, who demonstrated that the Penman equation tends to exaggerate\nsurface dryness. However, while the PET overestimation is most prominent in\ndrylands, tree cover expansion mainly occurs in humid regions in our global\nforestation experiments. In this regard, the PET overestimation associated with the\nPenman-Monteith model is expected to exert a limited impact on the decomposition\nof runoff changes. Using PETe within the Budyko framework, we confirmed that the\nspatial patterns of runoff changes due to atmospheric feedback (∆𝑅 ) and land-\n𝑎𝑡𝑚\nsurface change (∆𝑅 ) remain consistent with those derived from the Penman-based\n𝑙𝑎𝑛𝑑\nPET (Fig. R19).\nIt is however noteworthy that afforestation influences PET not only by increasing\nabsorbed shortwave radiation (i.e., available energy) due to a darker surface, but also\nby elevating near-surface humidity and thereby reducing vapor pressure deficit (Fig.\nR20). The energy-balance approach (PETe), which does not fully capture the effect of\nnear-surface humidity changes, is deficient in representing the PET reduction induced\nby surface moistening in tropical regions. In view of this, there is a potential for\nafforestation-induced PET decreases in the tropics to be misrepresented as apparent\nincreases when assessed with PETe (Fig. R21). Therefore, in the main text we\nretained the PET estimates based on the Penman-Monteith equation, while including\nthe PETe-based results in the Supplementary Information to support the robustness of\nour results to different PET calculation methods. In the revised manuscript, we now\ninclude details of the PETe calculation and discuss its support of our main results:\n“However, the Penman equation may overestimate PET because the underlying\nassumption of a saturated land surface is typically not satisfied. To address this issue,\nwe also calculated PET using an energy-based approach proposed by Zhou and\nYu116,117 (referred to as PETe). PETe represents the maximum ET constrained by the\nenergy supply (Rn) under conditions of saturated surface. PETe is calculated using\nthe net radiation (Rn) and the wet Bowen ratio (β ), given by:\nw\n𝑅\n𝑛\n𝑃𝐸𝑇 = (7)\n𝑒 𝜆(1+𝛽 )\n𝑤\nβ is estimated based on the aerodynamics, that is, the ratio of sensible over latent\nw\nheat exchanged between the saturated surface and the atmosphere, given by:\n𝛾(𝑇 −𝑇 )\n𝑠 𝑎\n𝛽 = (8)\n𝑤 𝑒∗ −𝑒\n𝑠 𝑎\nwhere 𝑇 −𝑇 is the difference between surface (𝑇 ) and air (𝑇 ) temperatures, and\n𝑠 𝑎 𝑠 𝑎\n𝑒∗−𝑒 is the difference between vapor pressure at the saturated surface (𝑒∗) and that\n𝑠 𝑎 𝑠\nof the ambient air (𝑒 ).” (Lines 705-716 in Methods);\n𝑎\n“The decomposition of runoff changes was repeated using outputs from the final 60\nyears of the equilibrium simulations with two Penman-type PET equations (Penman-\nOW and PM-RC) and an energy-balance-based PET (PETe). All three approaches\nyielded consistent attribution results, supporting our main conclusion that forest-\natmosphere feedbacks dominate the latitudinally contrasting runoff responses to\nglobal forestation (Supplementary Fig. 10). This consistency is present despite the\ntendency of greater Penman-based PET than PETe particularly in arid regions\n(Supplementary Fig. 26), as also noted by Zhou and Yu116,117. This PET difference has\nminor influence on our results (Supplementary Fig. 10) likely because potential forest\nexpansion is mainly located in humid regions. Moreover, the Penman equations\nexplicitly represent afforestation-induced increases in near-surface humidity and its\nsuppression on atmospheric moisture demand — an effect that the energy-balance\napproach cannot fully capture (Supplementary Figs. 13a, c, e and 16). As the\nhumidity-related effects are an important component of atmospheric feedbacks, our\nmain analyses of runoff change decomposition are based on Penman-derived PET,\nwhile the results using PETₑ are presented in the Supplementary Information.” (Lines\n752-766 in Methods);\n“Furthermore, applying alternative PET estimation methods — including two\nPenman-based equations and another energy-balance approach — generates highly\nconsistent results, further strengthening the reliability of our findings (Methods,\nSupplementary Fig. 10).” (Lines 258-261 in Results).\nFig. R18 (also appears as Supplementary Fig. 27 in the revised version) Comparison\nbetween PET calculated using the Penman–Monteith equation and that derived from\nthe energy balance approach (PETe). (a) Difference between PET estimated by the\nPenman–OW formulation (PETow) and PETₑ (PETow – PETe); (b) Difference\nbetween PET estimated by the PM–RC formulation (PETrc) and PETe (PETrc –\nPETe).\nFig. R19 (also appears as Supplementary Fig. 10 in the revised version) Runoff\nchange associated with forest-atmosphere feedbacks (∆𝑅 ) and land surface changes\n𝑎𝑡𝑚\n(∆𝑅 ) using different PET methods. (a, c, e) Spatial patterns of ∆𝑅 using\n𝑙𝑎𝑛𝑑 𝑎𝑡𝑚\nPenman-OW, PM-RC and the energy balance approach (PETe), respectively. (b, d, f)\nSpatial patterns of ∆𝑅 using Penman-OW, PM-RC and the energy balance\n𝑙𝑎𝑛𝑑\napproach (PETe), respectively.\nFig. R20 (also appears as Supplementary Fig. 16 in the revised version) Spatial\npatterns of global forestation-induced changes in surface net radiation (Δ(R -G), a),\nn\nvapour pressure deficit (ΔVPD, b). Black stipples denote statistically significant\nchange at the 5% significance level using the paired sample t-test.\nFig. R21 (also appears as Supplementary Fig. 13a, c, e in the revised version) Spatial\npatterns of changes in potential evapotranspiration (PET) using different PET\nmethods. (a) Penman-OW (∆𝑃𝐸𝑇 ), (b) PM-RC (∆𝑃𝐸𝑇 ), (c) the energy balance\n𝑜𝑤 𝑟𝑐\napproach (∆𝑃𝐸𝑇 ). Black stipples denote statistically significant change at the 5%\n𝑒\nsignificance level using the paired sample t-test.\n[Reviewer #2 Major Comment 8]\nAdditionally, how have the authors accounted for internal climate variability in the\nBudyko attribution?\n[Response] Thank you for this valuable comment. We appreciate this opportunity to\nclarify how internal climate variability was accounted for in our attribution. In the\nBudyko framework, there is an assumption of steady state of the catchments’ water\nbalance. Our equilibrium simulation perfectly meets this assumption, though there is\nstill strong internal climate variability in the time series. In our original analysis, we\ntook the average of the last 30 years of the 100-year simulation, expected to also\naverage out the internal climate variability.\nWith your comment, we recognized that the 30-year averaging window, while\neffective for averaging out interannual to inter-decadal variability, may not eliminate\nthe influence of multi-decadal oscillations. To further control for potential influence\nof multi-decadal climate variability, and considering that the simulations reached an\nequilibrium after about 40 years, now in the revised manuscript, we analyze the last\n60 years of the equilibrium period. The new results remain consistent with our\noriginal finding of contrasting forestation-driven runoff changes in tropical versus\nhigh-latitude regions (see new figures in the revised manuscript). This indicates that\nour main results are robust to the additional exclusion of multi-decadal oscillations.\nWe have explained the reason for selecting the final 60 years of data in the revised\nMethods section, as follows: “selecting 60 years also helps reduce the potential\ninfluence of inter-decadal and multi-decadal climate variability” (Lines 574-575).\nFurthermore, to more comprehensively assess the potential effects of climate\nvariability across different timescales, we implemented a bootstrap resampling\napproach using data from the last 60 years of the equilibrium period. Specifically, we\nperformed 1,000 random resamplings (with replacement) of 30-, 40-, 50-, and 60-year\nsubsets, each time decomposing runoff changes into the atmospheric feedback and\nland surface components based on the Budyko framework. The bootstrap ensemble\nmeans derived from 30-, 40-, 50-, and 60-year subsets all yield consistent results (Fig.\nR22), demonstrating that internal climate variability has minimal impact on our\nprimary findings. This methodology is now detailed in the revised Methods, and the\nassociated results have been incorporated into the new version, which reads:\n“In addition to the use of 60-year equilibrium simulations, we further examined how\nclimate variability across different timescales might affect the attribution of runoff\nresponses. To this end, we implemented a bootstrap resampling approach to generate\n1,000 random year samples (with replacement from the final 60 years), for 30-, 40-,\n50-, and 60-year timescales, respectively. For each sample, the forestation-induced\nrunoff change was calculated as the difference between the SCE and CTL simulations\nand further decomposed into atmospheric feedback and land-surface components\nusing the Budyko framework. The mean of the 1,000 realizations represents the\naverage runoff response for each timescale. The ensemble means derived from the\n30-, 40-, 50-, and 60-year subsets produced results consistent with our main findings\n(Supplementary Fig. 9), indicating that internal climate variability has minimal\ninfluence on our conclusions.” (Lines 768-778 in Methods);\n“In addition, we assessed how inter-decadal to multi-decadal climate variability\ninfluence the attribution results by varying time windows of the equilibrium\nsimulations based on a bootstrap approach (Methods). The latitudinal pattern of\nrunoff responses and the leading role of forest-atmosphere feedbacks are consistent\nacross all time windows, indicating negligible influence of internal climate variability\non our results (Supplementary Fig. 9).” (Lines 253-258 in Results).\nFig. R22 (also appears as Supplementary Fig. 9 in the revised version) The influence\nof internal climate variability on afforestation-induced runoff changes and their\ndriving processes across different timescales. (a, d, g, j) Spatial patterns of forestation-\ninduced runoff changes based on the 30-, 40-, 50-, and 60-year subsets, respectively,\nderived with a bootstrap resampling approach. (b, e, h, k) Spatial patterns of runoff\nchanges caused by forest-atmosphere feedbacks (∆𝑅 ) assessed with the Budyko\n𝑎𝑡𝑚\nframework. (c, f, i, l) Spatial patterns of runoff changes caused by land surface\nchanges (∆𝑅 ) assessed with the Budyko framework.\n𝑙𝑎𝑛𝑑\n[Reviewer #2 Major Comment 9]\nThe Budyko attribution approach primarily considers first-order controls (mean\nclimate and land surface characteristics) on runoff. Yet, forestation-induced changes in\nprecipitation characteristics (e.g., rainfall intensity, fraction of precipitation as snow)\nmay substantially alter runoff generation processes and soil moisture availability for\nevapotranspiration.\n[Response] We very much appreciate this constructive feedback, which motivated us\nto reconsider the influence of precipitation characteristics on runoff generation within\nthe Budyko attribution framework. As you kindly pointed out, the Budyko framework\nprimarily accounts for mean climatic conditions (precipitation and PET) and land\nsurface characteristics, and in the data-based approximation of n, the effects of\nprecipitation characteristics (e.g., rainfall intensity, fraction of snowfall) are implicitly\nincorporated into this land surface term. This limitation, which is well recognized\nwithin the ecohydrology community, may bias our interpretation of n changes as\nsolely resulting from land surface processes in our attribution of forestation-induced\nrunoff changes.\nTo address potential bias related to precipitation characteristics, we separated\nprecipitation and runoff changes due to altered precipitation characteristics and\nincorporated these into effects of atmospheric changes, following the approach\nproposed by Zhou et al.25. We further decomposed the effects of the atmospheric\nfeedbacks (due to altered mean precipitation, PET, and precipitation characteristics),\nand land surface changes (only due to catchment properties) on runoff under the\nmodified Budyko framework. In particular, this modification ensures applicability in\ncold regions because snow-related biases have been corrected. The results based on\nthis extended method are consistent with our main conclusions, showing that forest-\natmosphere feedbacks primarily drive the latitudinal contrast in afforestation-induced\nrunoff changes, with decreases at high latitudes and increases in the tropics (Fig.\nR23). In the revised MS, we have added a detailed description of this Budyko\nattribution method with correction to effects of precipitation characteristics and of\nhow this modification supports our main conclusions. The added text reads as\nfollows:\n“Finally, we assessed the potential influence of forestation-induced changes in\nprecipitation characteristics (e.g., rainfall intensity, snowfall fraction) on the Bydyko-\nbased attribution of runoff changes. In cold regions, for example, a considerable\nportion of precipitation falls as snow during winter, and the subsequent spring or\nearly-summer snowmelt substantially contributes to seasonal runoff. Since the\nstandard Budyko framework only accounts for the first-order control of mean climate\n(precipitation and PET) and land surface conditions, the effects of precipitation\ncharacteristics on runoff (e.g., through the inter-seasonal processes) were implicitly\nconflated into the data-approximated n (ref.69). Hence, this may bias our\ninterpretation of n changes as resulting from altered land surface properties. To\naddress this bias, we adopted a modified Budyko-based approach for attribution\nfollowing Zhou et al.61, which isolates the portion of precipitation change solely\ndriven by altered precipitation characteristics (𝑃∗). In this approach, the 𝑃∗-related\nportion of precipitation is artificially partitioned into runoff and directly subtracted\nfrom total 𝑃∗ and runoff, to present its effect being conflated into catchment\nproperties (n) (Eqn. 17). This method does not consider the potential effect of\nforestation-induced changes in land surface properties on runoff generation,\nimplicitly assuming that runoff generation depends only on precipitation\ncharacteristics. Therefore, the ratio of the remaining runoff (𝑅 −𝑃∗) to the\n2\nremaining precipitation (𝑃 −𝑃∗) in the forestation experiment is then assumed to be\n2\nidentical to that in the control simulation:\n𝑅\n2\n−𝑃∗ 𝑅\n2′\n𝑅\n1\n= = (17)\n𝑃 −𝑃∗ 𝑃 𝑃\n2 2′ 1\nwhere subscripts 1 and 2 denote the CTL and SCE simulations, respectively, and\nsubscript 2′ suggests that the effect of precipitation characteristics (𝑃∗) has been\nexcluded for the forestation scenario. Solving for 𝑃∗ yields:\n𝑅 ×𝑃 −𝑅 ×𝑃\n𝑃∗ = 2 1 1 2 (18)\n𝑃 −𝑅\n1 1\nFurther, the effects of changes in the remaining precipitation (𝑃 ), PET, and n on the\n2′\nremaining runoff (𝑅 ) are decomposed under the Budyko framework. This allowed us\n2′\nto identify the more realistic climate change effect (changes in the mean P, PET and\nprecipitation characteristics) and land surface effect on forestation-induced runoff\nchanges:\n1 𝜕𝐸𝑇 𝜕𝐸𝑇\n∆𝑅′ = − [( ) +( ) −2](𝑃 −𝑃 )\n𝑎𝑡𝑚 2 𝜕𝑃 𝜕𝑃 2′ 1\n1 2′ (19)\n1 𝜕𝐸𝑇 𝜕𝐸𝑇\n− [( ) +( ) ](𝑃𝐸𝑇 −𝑃𝐸𝑇 )+𝑃∗\n2 𝜕𝑃𝐸𝑇 𝜕𝑃𝐸𝑇 2 1\n1 2′\n1 𝜕𝐸𝑇 𝜕𝐸𝑇\n∆𝑅 = − [( ) +( ) ](𝑛 −𝑛 ) (20)\n𝑙𝑎𝑛𝑑 2 𝜕𝑛 𝜕𝑛 2′ 1\n1 2′\nTogether with the standard Budyko-based attribution, this extended approach, which\nexplicitly accounts for precipitation characteristics, provides a refined estimate for\nthe contributions of atmospheric feedbacks and land surface changes. Results from\nthis sensitivity test remain consistent with our main findings, confirming that forest-\natmosphere feedbacks predominantly drive the latitudinal contrast in runoff responses\nto global afforestation.” (Lines 779-808 in Methods section).\n“While the Budyko-based attribution highlights the dominant role of forest-\natmosphere feedbacks in driving runoff responses, it does not explicitly account for\nthe influence of altered precipitation characteristics, such as rainfall intensity and\nsnow fraction, on runoff generation processes69. To address this potential bias, we\nadopted a modified approach following Zhou et al.61, which isolates the effects of\naltered precipitation characteristics and incorporates these into atmospheric\nfeedbacks (Methods). The results from this modified method align well with our main\nfindings, confirming that forest-atmosphere feedbacks predominantly drive the\nlatitudinal divergence of forestation-induced runoff changes (Supplementary Fig. 8).”\n(Lines 245-253 in Results section)\nFig. R23 (also appears as Supplementary Fig. 8 in the revised version) The attribution\nof forestation-induced runoff changes based on the modified Budyko method with\nexplicit accounting for precipitation characteristics. (a) Spatial patterns of runoff\nchanges caused by forestation-induced alterations in precipitation characteristics. (b)\nSpatial patterns of runoff changes caused by forestation-induced alterations in mean\nprecipitation and PET changes. (c) Spatial patterns of runoff changes caused by\nforest-atmosphere feedbacks, representing the combined effects of mean P, PET and\nprecipitation characteristics (∆𝑅 ). (d) Spatial patterns of runoff changes caused by\n𝑎𝑡𝑚\nland surface changes (∆𝑅 ).\n𝑙𝑎𝑛𝑑\n[Reviewer #2 Major Comment 10]\nFinally, can the authors provide observational evidence to support their model-based\nfindings? Robustness checks relying solely on model-derived data are less persuasive;\nvalidation against independent observations is critical to establish confidence.\n[Response] We appreciate this useful comment which motivated us to validate our\nmodel-based results against independent observations. We agree that observational\nevidence can strengthen confidence in our modeled responses. However, this\nvalidation is precluded by the unavailable, or unreliable, observations of vegetation\nfeedbacks on the climate system. The signal of climate feedbacks is often intertwined\nwith vegetation responses to climate. Many observation-based studies instead used the\nspace-for-time substitution to quantify potential climate feedbacks of land cover\nchanges, assuming that forested and non-forested landscapes share similar\natmospheric backgrounds. This assumption neglects the influence of forest change on\natmospheric circulation, causing remote hydrological responses by redistributing\nmoisture and changing precipitation patterns. Given such limitations of conventional\nobservation-based methods, there is no ground truth for validating our modelled\nhydrological feedbacks.\nNonetheless, for regions where precipitation sources largely from local ET (i.e., with\nstrong local moisture recycling), the signature of forest-atmosphere feedbacks might\nbe detectable in observational data. To explore this possibility, we analyzed the spatial\nsensitivity of surface moisture balance (P minus ET) to tree cover. In this analysis, we\nemployed precipitation from the Multi-Source Weighted-Ensemble Precipitation\n(MSWEP, an integration of gauge, satellite, and model data) dataset (1°×1°\nresolution), ET from the Global Land Evaporation Amsterdam Model (GLEAM)\ndataset, and forest cover maps from the European Space Agency Climate Change\nInitiative (ESA CCI) 300 m land cover dataset. Specifically, for each 1° × 1°\nvegetated grid cell, we performed spatial moving-window (3×3) regressions of\nprecipitation (or ET) against forest cover, near-surface air temperature (from Climatic\nResearch Unit, CRU), and incoming shortwave radiation (from Clouds and the Earth’s\nRadiant Energy System, CERES) to estimate the precipitation (or ET) sensitivity to\nforest cover fraction. The difference between precipitation and ET sensitivities\nrepresents the sensitivity of P–ET to forest cover change, which approximates runoff\nover long-term timescales. We then selected pixels where both precipitation and ET\nsensitivities to forest change were statistically significant (p < 0.05).\nFig. R24 (also appears as Supplementary Fig. 6 in the revised version) Observation-\nestimated sensitivity of precipitation minus evapotranspiration (P–ET) to forest cover\nchanges. The values are binned according to latitude: high latitudes (>60°), mid-\nlatitudes (30°-60°), and low latitudes (0-30°). The boxplots show the median\n(horizontal lines), 25th, 75th quartiles, maximum and minimum values (top\nand bottom of the whiskers) and outliers (dots) of P–ET sensitivity. The number displayed\nabove each bin indicates the total count of grid cells in which both the local\nprecipitation and ET are both statistically sensitive to forest cover change (p < 0.05).\nThis observation-based analysis shows a clear latitudinal contrast: P–ET overall\ndecreases with forest cover gain in most high-latitude regions but increases in the\ntropics (Fig. R24), consistent with our model-based findings. There are a considerable\nfraction of grids showing opposing signs, which is likely because this empirical\napproach cannot fully remove the moisture redistribution effects of circulation.\nMoreover, Ma et al.26 similarly estimated the sensitivity of P–ET to forest cover\nchanges by directly regressing P–ET against forest cover using observations. Their\nresults showed that historical deforestation mainly decreased P–ET in the tropics\nwhile increasing it in high-latitude regions. Although the hydrological responses to\ndeforestation and afforestation are asymmetric in magnitude, they generally present\nopposite signs. Therefore, the results of Ma et al.26 provide additional support for the\nrobustness of our findings. We have included this observational verification in the\nMethods and discussed its support for our model-based conclusions in the main text.\nThe added texts read as follows:\n“Observed sensitivity of P–ET to forest cover changes\nTo quantify the observation-based sensitivity of P–ET to forest cover change, we used\nprecipitation data from the Multi-Source Weighted-Ensemble Precipitation (MSWEP;\n1° × 1° resolution), ET data from the Global Land Evaporation Amsterdam Model\n(GLEAM; 0.1° × 0.1°), forest cover data from the European Space Agency Climate\nChange Initiative (ESA CCI; 30 m), near-surface air temperature from the Climatic\nResearch Unit Time-Series (CRU TS; 0.5° × 0.5°), and incoming shortwave radiation\nfrom the Clouds and the Earth’s Radiant Energy System (CERES; 1° × 1°).\nConsidering the role of mesoscale and large-scale circulation in redistributing\natmospheric moisture, which can obscure the forest-precipitation signal, we analyzed\n20-year means (2001–2020) rather than individual years. All datasets were resampled\nto a common 1° × 1° grid to match the coarser spatial resolution of model outputs.\nFor each vegetated grid cell, we performed regressions of precipitation (or ET) \nagainst forest cover, air temperature, and incoming shortwave radiation within a 3 ×\n3 window centred on the pixel, to estimate the sensitivity of precipitation (or ET) to\nforest cover change. The difference between precipitation and ET sensitivities\nrepresents the sensitivity of P–ET to forest cover changes, which approximates runoff\nresponses over long-term scales. It is important to note that most observation-based\nspace-for-time substitution approaches assume that forested and non-forested\nlandscapes share similar atmospheric backgrounds, and thus cannot capture the\ninfluence of forest change on atmospheric circulation that redistributes moisture and\naffects precipitation. We hypothesize that this moisture redistribution effects of\ncirculation diminishes the local forest–precipitation signal. Hence, we only selected\npixels where precipitation and ET sensitivities to forest change were both statistically\nsignificant (p < 0.05), despite their limited number” (Lines 660-684 in Methods\nsection);\n“To further verify our simulated latitudinal gradient of runoff responses to forestation,\nwe calculated the observed sensitivity of precipitation minus ET (P–ET) to forest\ncover changes based on spatial regression, and retained grid cells where precipitation\nand ET both exhibit significant sensitivity to forest cover changes (Methods). Over\nmulti-decadal timescales, P–ET serves as an approximate estimate for runoff.\nThis observation-based analysis shows a clear latitudinal contrast: P–ET overall\ndecreases with forest cover gain in high-latitude regions but increases in the tropics\n(Supplementary Fig. 6), in agreement with our model-based findings.” (Lines 205-212\nin Results section)\n[Reviewer #3 General Comment]\nI am pleased this subject is being explored as it is a poorly assessed aspect of the\nIPCC Reports. This manuscript should be accepted subject to satisfactory responses to\nthe comments below. This is an excellent paper.\n[Response] We sincerely thank the reviewer for the positive evaluation and\nencouraging comments. We are very grateful for your recognition of the importance\nof this topic and the quality of our work. We have carefully addressed all the\ncomments raised and revised the manuscript accordingly.\n[Reviewer #3 Major Comment 1]\n1. The authors use the Budyko approach to estimate the integrated effects of\nprecipitation, evapotranspiration (ET), potential evapotranspiration (PET) and\nlandscape characteristics on aridity and runoff. This is a valuable analysis approach.\nHowever, while hydrologists use ET and PET, separating into physical evaporation\n(such as from soils and water bodies) and transpiration from plants through stoma\nwould strengthen (and likely add a new perspective to) the analysis. I recommend\nadded text on this wrt needed follow on research.\nHere are papers that discuss this issue\nMiralles, D.G.; Brutsaert, W.; Dolman, A.J.; Gash, J.H. On the Use of the Term\n“Evapotranspiration”. Water Resour. Res.2020, 56\nPielke Sr., R.A., D. Peters, and D. Niyogi, 2022: Ecology and climate of the Earth -\nthe same biogeophysical system. Climate, 10, 25, https://doi.org/10.3390/cli10020025\n[Response] Thank you for this insightful comment, which prompted us to examine\nthe individual components of ET in our afforestation experiments. In our analysis,\nmodelled ET changes were decomposed into the four processes: transpiration, canopy\ninterception, soil evaporation, and sublimation (Fig. R25). Our results show that\nforestation-induced ET increases primarily result from enhanced canopy transpiration\nand interception, though partly offset by a simultaneous reduction in soil evaporation.\nThis reduction occurs because the expanded canopy cover shades the soil surface,\nreducing available energy for evaporation, and decreases near-surface vapor pressure\ndeficit that drives soil evaporation44. In addition, afforestation leads to a slight but\nsignificant increase in sublimation at mid-to-high latitudes in the Northern\nHemisphere, likely due to enhanced snow interception by the expanded forest canopy.\nFig. R25 (also appears as Supplementary Fig. 3 in the revised version) Spatial patterns\nof forestation-induced changes in the four ET components, including transpiration (a),\ninterception (b), soil evaporation (c) and sublimation (d).\nIn the revised manuscript, we have incorporated a sentence in the Results section to\nelucidate how afforestation affects these ET components to better understand total ET\nresponse, written as: “The vegetation-driven ET increases are primarily attributed to\nenhanced canopy transpiration and interception, partially offset by reduced soil\nevaporation (Supplementary Fig. 3).” (Lines 139-141).\n[Reviewer #3 Major Comment 2]\n2. There has been significant greening over the last decades. Have the authors\nconsidered this? I recommend discussing.\nZhu, Z.; Piao, S.; Myneni, R.B.; Huang, M.; Zeng, Z.; Canadell, J.G.; Ciais, P.; Sitch,\nS.; Friedlingstein, P.; Arneth, A.; et al. Greening of the Earth and its drivers. Nat.\nClim. Change2016, 6, 791–795\n[Response] We appreciate this thoughtful comment about the extensive greening\nobserved over recent decades. The connection between greening and afforestation has\nbeen well established in the literature, for instance, Chen et al. demonstrated that\nChina’s large-scale tree restoration contributed greatly to the global greening trend,\nwith forests accounting for 42% of the country’s greening45. While connected, the two\nphenomena differ mechanistically: greening is a gradual increase in leaf area mainly\ndriven by natural factors such as CO fertilization and warming in cold regions,\n2\nwhereas afforestation represents an abrupt transition from non-forest to forest, driven\nby direct human intervention. Given that our study focuses on the deliberate policy-\nrelevant intervention of afforestation, we did not directly link our assessment to\nnatural greening trends. Given their shared mechanistic basis of hydrological\nfeedbacks (i.e., both through enhancing ET), we have now added a comparative\ndiscussion on their hydrological effects. In specific, Zeng et al.46, also based on IPSL\nmodel simulations, found that the observed greening over the past three decades\nincreased runoff in the tropics but reduced it in Siberia, consistent with our\nafforestation-related results. Globally, the greening-induced increases in ET and\nprecipitation during the historical period are about one-third to one-half of those\nsimulated in our full-potential afforestation experiment. The new discussion linking\nhydrological feedbacks of greening and afforestation now reads: “The latitudinal\ndivergence in forestation-induced runoff responses aligns with an early modeling\nstudy on the hydrological effects of historical Earth greening, which also showed\nincreased runoff in the tropics but reductions in Siberia80. However, changes of global\nwater fluxes (in both ET and precipitation) induced by forestation is two to three times\nlarger than that associated with historical greening” (Lines 448-452).\n[Reviewer #3 Major Comment 3]\n3. The spatial resolution of their models is too coarse to evaluate accurately the role of\nmost regional spatial heterogeneity, and no mesoscale landscape structure.\nPatchiness of deforestation nonlinearly affects the amount and intensity of cumulus\ndevelopment, for example, and thus rainfall and then runoff. In high latitudes it affects\nsnow melt also.\nHere are two studies of that effect.\nAvissar, R., and Y. Liu, Three-dimensional numerical study of shallow convective\nclouds and precipitation induced by land surface forcing, J. Geophys. Res., 101,\n7499–7518, 1996.\nAnthes 1984: Enhancement of convective precipitation by mesoscale\nvariations in vegetative covering in semiarid regions. J. Clim.\nAppl. Meteor., 23, 541-554\n[Response] Many thanks for this important point regarding the inherent limitations of\nclimate models in capturing mesoscale feedbacks. We agree that our model, and also\nother coarsely resolved global climate models (GCMs), cannot explicitly represent the\nfine-scale, and possibly nonlinear interactions between forest patchiness, convective\ndevelopment, and precipitation processes, as highlighted by seminal works on\nmesoscale land-atmosphere feedbacks47–52.\nHowever, the choice of model resolution is inherently tied to the focused scale of the\nstudy. For small-scale afforestation (e.g., local or watershed-level projects), higher-\nresolution regional climate models (e.g., the Weather Research and Forecasting\n[WRF] model) are more appropriate. Such models resolve mesoscale landscape\nstructures (e.g., forest patchiness) and explicitly capture local convective processes\nand snowmelt dynamics, and thus enables evaluating how fine-scale spatial\nheterogeneity modulates hydrological responses (e.g., localized rainfall or runoff).\nIn contrast, our study focuses on continental-scale afforestation, where the primary\nobjective is to quantify large-scale, first-order hydroclimatic effects (e.g., basin-wide\nmoisture recycling and continental-scale changes in precipitation regimes). For large-\nscale afforestation, such as the most extensive case in China, the hydrological effects\nare less sensitive to mesoscale processes. For example, previous modeling studies\nbased on a regional climate model (40-km resolution)35 or a high-resolution zoomed\nIPSL-CM model41 have suggested enhanced runoff in response to China’s forest\nexpansion, in line with our GCM-based results.\nIn light of your comment, we explicitly acknowledge in our new manuscript the\nincapability of our GCM in representing mesoscale processes. In specific, we clarified\nthat although deforestation tends to reduce precipitation at broader scales,\nobservations indicate that it can enhance local convection and rainfall over cleared\nareas by inducing upward motion and the inflow of moist air from adjacent forested\nregions49,51,53. These findings suggest that mesoscale landscape heterogeneity may\nmodulate the spatial distribution of hydrological feedbacks induced by forest cover\nchange. In view of this, we emphasized the need for high-resolution regional climate\nmodels capable of resolving convective processes to further quantify the role of\nmesoscale landscape structure in shaping hydroclimatic outcomes of local or regional\nafforestation. The added text is as follows: “Finally, our global climate models\n(GCMs) with coarse resolution are deficient in representing mesoscale feedbacks,\nbut they are capable of capturing the first-order hydroclimatic effects of large-scale\nforestation. For example, previous modeling studies based on a regional climate\nmodel (40-km resolution)90 or a zoomed IPSL-CM have suggested enhanced runoff in\nresponse to historical afforestation in China40, consistent with our GCM-based\nresults. Nevertheless, GCMs cannot explicitly resolve fine-scale (10–20 km),\npotentially nonlinear interactions among forest patchiness, convective development,\nand local precipitation98–102. Therefore, for local‐scale afforestation studies,\nregional climate models (RCMs) with kilometer‐scale grid spacing are recommended to\nbetter understand the role of mesoscale landscape structure in shaping the local\nhydrological outcomes of afforestation.” (Lines 529-538)\n[Reviewer #2 Major Comment 4]\n4. Their resolution of 1.26 degrees by 2.5 degrees and 1.9 degrees by 2.5 degrees are\nobviously insufficient to resolve features smaller than 2 delta X ( more accurately 4\ndelta X).\nIndeed, forestation that is spaced on spatial scales smaller than that is likely to result\nin an enhancement of precipitation, than a uniform cover as cited earlier. The authors\nshould recommend a follow up study with high spatial resolution global climate\nmodels. They don’t need to be long term or equilibrium runs.\n[Response] Thank you for your clarification and for emphasizing the coarse\nresolution issue of GCMs. As you kindly point out, afforestation distributed in a\npatchy manner at finer scales may influence precipitation through mesoscale\ncirculation adjustments and convective development, which are indeed not captured in\nour simulations. As noted in our response to Comment 3, coarse-resolution GCMs are\nused because they are able to capture the hydrological responses to continental or\nglobal forest changes, and are computationally feasible. To adequately address issues\nrelated to landscape heterogeneity and patchiness, we recommend follow-up studies\nusing high-resolution regional climate models (RCMs), such as the WRF model, as\nsuch tools are capable of resolving the convective-scale processes necessary to\nevaluate how fine-scale spatial heterogeneity modulates hydrological responses. We\nhave incorporated this recommendation into the Discussion section of our revised\nmanuscript (Lines 529-538), as also detailed in our response to your last comment.\n[Reviewer #2 Major Comment 5]\n5. The authors might find it useful to cite a 2005 NRC report which, while essentially\nignored by the IPCC, has executive findings that advocated for the type of study\npresented in the submitted manuscript\nNational Research Council, 2005: Radiative forcing of climate change: Expanding the\nconcept and addressing uncertainties. Committee on Radiative Forcing Effects on\nClimate Change, Climate Research Committee, Board on Atmospheric Sciences and\nClimate, Division on Earth and Life Studies, The National Academies Press,\nWashington, D.C., 208 pp.\nHere are excerpts from the findings\n“Regional variations in radiative forcing may have important regional and global\nclimatic implications that are not resolved by the concept of global mean radiative\nforcing. Tropospheric aerosols and landscape changes have particularly\nheterogeneous forcings. To date, there have been only limited studies of regional\nradiative forcing and response. Indeed, it is not clear how best to diagnose a regional\nforcing and response in the observational record; regional forcings can lead to global\nclimate responses, while global forcings can be associated with regional climate\nresponses. Regional diabatic heating can also cause atmospheric teleconnections that\ninfluence regional climate thousands of kilometers away from the point of forcing.\nImproving societally relevant projections of regional climate impacts will require a\nbetter understanding of the magnitudes of regional forcings and the associated\nclimate” and\n“Several types of forcings—most notably aerosols, land-use and land-cover change,\nand modifications to biogeochemistry—impact the climate system in nonradiative\nways, in particular by modifying the hydrological cycle and vegetation dynamics.\nAerosols exert a forcing on the hydrological cycle by modifying cloud condensation\nnuclei, ice nuclei, precipitation efficiency, and the ratio between solar direct and\ndiffuse radiation received. Other nonradiative forcings modify the biological\ncomponents of the climate system by changing the fluxes of trace gases and heat\nbetween vegetation, soils, and the atmosphere and by modifying the amount and types\nof vegetation. No metrics for quantifying such nonradiative forcings have been\naccepted. Nonradiative forcings have eventual radiative impacts, so one option would\nbe to quantify these radiative impacts. However, this approach may not convey\nappropriately the impacts of nonradiative forcings on societally relevant climate\nvariables such as precipitation or ecosystem function. Any new metrics must also be\nable to characterize the regional structure in nonradiative forcing and climate\nresponse”\nSee also\nKabat, P., Claussen, M., Dirmeyer, P.A., J.H.C. Gash, L. Bravo de Guenni, M.\nMeybeck, R.A. Pielke Sr., C.J. Vorosmarty, R.W.A. Hutjes, and S. Lutkemeier,\nEditors, 2004: Vegetation, water, humans and the climate: A new perspective on an\ninteractive system.Springer, Berlin, Global Change - The IGBP Series, 566 pp.\n[Response] Thank you very much for bringing the National Research Council (2005)\nreport54 and the book by Kabat et al. (2004)55 to our attention. These highly relevant\nreports provide inspiring perspectives on the important role of regional and non-\nradiative forcings—particularly land-use and land-cover change—in shaping the\nclimate system and the hydrological cycle. These perspectives have proved prescient\nover the past two decades, but the non-radiative forcing still receive insufficient\nattention in global mean radiative forcing frameworks. Our study provides direct\nsupport for this perspective by quantifying the large-scale hydrological impacts of\nafforestation as a non-radiative forcing. We have now cited the 2005 NRC report in\nour revised manuscript to further strengthen the significance of our research, which\nnow reads: “The latitudinal divergence in forestation-induced runoff responses aligns\nwith an early modeling study on the hydrological effects of historical Earth greening,\nwhich also showed increased runoff in the tropics but reductions in Siberia80.\nHowever, changes of global water fluxes (in both ET and precipitation) induced by\nforestation is two to three times larger than that associated with historical greening,\nindicating that forestation, as a form of non-radiative forcing, can strongly reshape\nthe global hydrological cycle, as consistently highlighted by numerous previous\nstudies81–85.” (Lines 448-454, refs. 84, 85).\n[Reviewer #2 Major Comment 6]\n6. I suggest other studies that further support their findings. Here are just a few\nexamples (and references therein)\nBetts, R. A. 2000 Offset of the potential carbon sink from boreal forestation by\ndecreases in surface albedo. Nature 408, 187-189.\nHossain, F., J. Arnold, E. Beighley, C. Brown, S. Burian, J. Chen, S. Madadgar, A.\nMitra, D. Niyogi, R.A. Pielke Sr., V. Tidwell, and D. Wegner, 2015: Local-to-regional\nlandscape drivers of extreme weather and climate: Implications for water\ninfrastructure resilience. J. Hydrol. Eng., 10.1061/(ASCE)HE.1943-5584.0001210 ,\n02515002.\nPielke, R.A. Sr., J. Adegoke, A. Beltran-Przekurat, C.A. Hiemstra, J. Lin, U.S. Nair,\nD. Niyogi, and T.E. Nobis, 2007: An overview of regional land use and land cover\nimpacts on rainfall. Tellus B, 59, 587-601, DOI:10.1111/j.1600-0889.2007.00251.x\nPielke Sr., R.A., A. Pitman, D. Niyogi, R. Mahmood, C. McAlpine, F. Hossain, K.\nGoldewijk, U. Nair, R. Betts, S. Fall, M. Reichstein, P. Kabat, and N. de Noblet-\nDucoudré, 2011: Land use/land cover changes and climate: Modeling analysis and\nobservational evidence. WIREs Clim Change 2011, 2:828–850. doi: 10.1002/wcc.144.\nPielke Sr., R.A., R. Mahmood, and C. McAlpine, 2016: Land’s complex role in\nclimate change. Physics Today, 69(11), 40, https://doi.org/10.1063/PT.3.3364\nRay, D.K., U.S. Nair, R.O. Lawton, R.M. Welch, and R.A. Pielke Sr., 2006: Impact of\nland use on Costa Rican tropical montane cloud forests. Sensitivity of orographic\ncloud formation to deforestation in the plains. J. Geophys. Res., 111, D02108,\ndoi:10.1029/2005JD006096.\n[Response] Thank you very much for bringing these relevant references to our\nattention. These studies provide solid evidence on the hydroclimatic and\nbiogeophysical consequences of land-use and land-cover changes, which is a key\ncomponent of non-radiative forcing on the climate system.\nSpecifically, Betts (2000)56 highlights the competing radiative effects of boreal\nafforestation due to changes in surface albedo, which partially offset the cooling effect\nof carbon sequestration; Pielke Sr. et al. (2007, 2011, 2016)57–59 provide a\ncomprehensive review of how land-use change influences precipitation and other\ncomponents of the hydrological cycle; Hossain et al. (2015)60 discuss the impacts of\nlandscape changes on extreme weather and hydrological processes at local to regional\nscales; and Ray et al. (2006)61 demonstrate the crucial role of mesoscale convective\nprocesses in deforestation-induced changes in precipitation.\nIn the revised manuscript, we have cited these suggested publications to better situate\nour results within the broader literature on biogeophysical climate feedbacks of land-\nuse and land-cover changes and to acknowledge prior contributions to understanding\nthe regional hydroclimatic effects of forest cover change. The sentences where these\nreferences are cited are listed as follows:\n“Previous studies indicate that, when moving from tropical to boreal regions, not only\ndoes the carbon sequestration potential of forests decline, but the biophysical effects\nalso shift from cooling to warming mainly due to a darkening of tree canopies,\ncautioning the limited climate benefits of afforestation in boreal regions6–10.” (Lines\n39-42, ref. 10: Betts (2000));\n“However, changes of global water fluxes (in both ET and precipitation) induced by\nforestation is two to three times larger than that associated with historical greening,\nindicating that forestation, as a form of non-radiative forcing, can strongly reshape\nthe global hydrological cycle, as consistently highlighted by numerous previous\nstudies81–85.” (Lines 450-454, refs. 81-83: Pielke Sr. et al. (2007, 2011, 2016));\n“Nevertheless, GCMs cannot explicitly resolve fine-scale (10–20 km),\npotentially nonlinear interactions among forest patchiness, convective development,\nand local precipitation98–102.” (Lines 534-536, ref. 98: Ray et al. (2006), ref. 102: Hossain et al.\n(2015)).\nReferences\n1. Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).\n2. Fesenmyer, K. A. et al. Addressing critiques refines global estimates of\nreforestation potential for climate change mitigation. Nat Commun 16, 4572 (2025).\n3. Veldman, J. W. et al. Comment on “The global tree restoration potential”. Science\n366, eaay7976 (2019).\n4. Bond, W. J., Stevens, N., Midgley, G. F. & Lehmann, C. E. R. The Trouble with\nTrees: Afforestation Plans for Africa. Trends in Ecology & Evolution 34, 963–965\n(2019).\n5. Di Sacco, A. et al. Ten golden rules for reforestation to optimize carbon\nsequestration, biodiversity recovery and livelihood benefits. 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(National Academies Press, Washington, D.C., 2005).\ndoi:10.17226/11175.\n55. Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive\nSystem. (Springer, Berlin, Heidelberg, 2004). doi:10.1007/978-3-642-18948-7.\n56. Betts, R. A. Offset of the potential carbon sink from boreal forestation by decreases\nin surface albedo. Nature 408, 187–190 (2000).\n57. PIELKE Sr, R. A. et al. An overview of regional land-use and land-cover impacts\non rainfall. Tellus B 59, 587–601 (2007).\n58. Pielke Sr., R. A. et al. Land use/land cover changes and climate: modeling analysis\nand observational evidence. WIREs Climate Change 2, 828–850 (2011).\n59. Pielke, R. A., Mahmood, R. & McAlpine, C. Land’s complex role in climate change.\nPhysics Today 69, 40–46 (2016).\n60. Hossain, F. et al. Local-To-Regional Landscape Drivers of Extreme Weather and\nClimate: Implications for Water Infrastructure Resilience. Journal of Hydrologic\nEngineering 20, 02515002 (2015).\n61. Ray, D. K., Nair, U. S., Lawton, R. O., Welch, R. M. & Pielke Sr., R. A. Impact of\nland use on Costa Rican tropical montane cloud forests: Sensitivity of orographic\ncloud formation to deforestation in the plains. Journal of Geophysical Research:\nAtmospheres 111, (2006)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily addressed all the issues that I raised in the last round of review. I thank the authors particularly for performing several additional analysis, including running different forestation scenarios. I recommend the manuscript for publication.", "reviewer_2": "The efforts by the authors have improved the manuscript sufficient for publication.", "reviewer_3": "The authors have provided a very detailed and thorough response to the reviews. They have addressed any concerns I have.\nThis is indeed a very important new paper that addresses a critical issue that has not been addressed in the IPCC reports. It will be widely cited.\nI recommend acceptance.\nMy only minor comment is for them to replace “resolution” with grid increment when discussing models. Resolution requires at least 4 grid intervals."}, "author_response": "Point-to-point responses to reviewer comments\nWe thank the three reviewers for their careful review and helpful comments/suggestions.\nBelow comments from these reviewers are copied, followed by our detailed responses\nin blue. Additional or altered text in the revised MS, when cited in this response\ndocument, is marked in red italics.\n[Reviewer #1 General Comment]\nThe authors have satisfactorily addressed all the issues that I raised in the last round of\nreview. I thank the authors particularly for performing several additional analysis,\nincluding running different forestation scenarios. I recommend the manuscript for\npublication.\n[Response] Thank you for your constructive feedback and positive assessment of our\nrevised manuscript. We greatly appreciate your recognition of the improvements made\nto strengthen the robustness of our results.\n[Reviewer #2 General Comment]\nThe efforts by the authors have improved the manuscript sufficient for publication.\n[Response] We thank the reviewer for the careful reassessment of our manuscript and\nfor the positive evaluation. We appreciate the reviewer’s constructive comments in the\nprevious round, which helped improve the quality and clarity of the manuscript.\n[Reviewer #3 General Comment]\nThe authors have provided a very detailed and thorough response to the reviews. They\nhave addressed any concerns I have.\nThis is indeed a very important new paper that addresses a critical issue that has not\nbeen addressed in the IPCC reports. It will be widely cited.\nI recommend acceptance.\n[Response] We sincerely thank the reviewer for the thorough review and the positive\nassessment. We also appreciate the recognition of the broader relevance of this work\nand the recommendation for acceptance.\n[Reviewer #3 Minor Comment 1]\nMy only minor comment is for them to replace “resolution” with grid increment when\ndiscussing models. Resolution requires at least 4 grid intervals.\n[Response] We thank the reviewer for pointing out the imprecise expression in our\ndiscussion of model resolution. Following the reviewer’s suggestion, we have replaced\n“resolution” with “grid increment” throughout the relevant text (e.g., Lines 526–529)."}]

Decision letter