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Neglecting land–atmosphere feedbacks overestimates climate-driven increases in evapotranspiration

Nature Climate Change volume 15, pages 1099–1106 (2025)Cite this article

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Abstract

Accurate projections of evapotranspiration (ET) are crucial for understanding hydrological responses to climate warming, which remain highly uncertain because of complex land–atmosphere interactions. Here we develop a theoretical framework to disentangle these interactions, achieving highly consistent ET projections between offline and coupled models. Our findings show that previous estimates of climate-driven ET increases have been exaggerated, primarily due to a substantial overestimation of atmospheric evaporative demand. Notably, the atmospheric conditions often assumed to drive ET are, in fact, responses to ET changes induced by soil moisture and vegetation dynamics. Neglecting these land–atmosphere feedbacks has led to a 25–39% overestimation of climate-driven global ET increases and a 77–121% exaggeration of the negative contribution from land surface changes. These biases have caused large discrepancies in hydrological projections and attributions between offline and coupled models, underscoring the importance of accurately representing land–atmosphere interactions to improve the reliability and consistency of future hydrological projections.

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Fig. 1: Contrasting relationships between ET and two PET estimators.
Fig. 2: Sensitivity of PETe and PETa to surface moisture and vegetation changes.
Fig. 3: Convergence of ET projections and attribution between offline and coupled models.
Fig. 4: Global pattern of projected ET changes and attribution results in CMIP6 models.

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Data availability

The Fluxnet2015 dataset is publicly available at https://fluxnet.org/data/fluxnet2015-dataset/, and the CMIP6 model simulations are publicly available at https://esgf-node.llnl.gov/search/cmip6/.

Code availability

The R code (version 3.5.1) used for data analyses is available via Zenodo at https://doi.org/10.5281/zenodo.16730362 (ref. 64).

References

  1. Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564 (2015).

    Article  Google Scholar 

  2. Zhou, S., Zhang, Y., Williams, A. P. & Gentine, P. Projected increases in intensity, frequency, and terrestrial carbon costs of compound drought and aridity events. Sci. Adv. 5, eaau5740 (2019).

    Article  Google Scholar 

  3. Allan, R. P. et al. Advances in understanding large‐scale responses of the water cycle to climate change. Ann. N. Y. Acad. Sci. https://doi.org/10.1111/nyas.14337 (2020).

  4. Zhang, S. et al. Reconciling disagreement on global river flood changes in a warming climate. Nat. Clim. Chang. 12, 1160–1167 (2022).

    Article  Google Scholar 

  5. Zhou, S., Yu, B. & Zhang, Y. Global concurrent climate extremes exacerbated by anthropogenic climate change. Sci. Adv. 9, eabo1638 (2023).

    Article  Google Scholar 

  6. Gu, B., Zhou, S., Yu, B., Findell, K. L. & Lintner, B. R. Multifaceted changes in water availability with a warmer climate. npj Clim. Atmos. Sci. 8, 31 (2025).

    Article  Google Scholar 

  7. Budyko, M. I. Climate and Life (Academic Press, 1974).

  8. Held, I. M. & Soden, B. J. Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686–5699 (2006).

    Article  Google Scholar 

  9. Milly, P. C. D. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).

    Article  Google Scholar 

  10. Greve, P., Roderick, M. L., Ukkola, A. M. & Wada, Y. The aridity index under global warming. Environ. Res. Lett. 14, 124006 (2019).

    Article  CAS  Google Scholar 

  11. Koutsoyiannis, D. Revisiting the global hydrological cycle: is it intensifying? Hydrol. Earth Syst. Sci. 24, 3899–3932 (2020).

    Article  Google Scholar 

  12. Milly, P. C. D. & Dunne, K. A. A hydrologic drying bias in water-resource impact analyses of anthropogenic climate change. J. Am. Water Resour. Assoc. 53, 822–838 (2017).

    Article  Google Scholar 

  13. Zaitchik, B. F., Rodell, M., Biasutti, M. & Seneviratne, S. I. Wetting and drying trends under climate change. Nat. Water 1, 502–513 (2023).

    Article  Google Scholar 

  14. Zhou, S., Yu, B., Huang, Y. & Wang, G. The complementary relationship and generation of the Budyko functions. Geophys. Res. Lett. 42, 1781–1790 (2015).

    Article  Google Scholar 

  15. Scheff, J., Coats, S. & Laguë, M. M. Why do the global warming responses of land‐surface models and climatic dryness metrics disagree? Earth’s Future 10, e2022EF002814 (2022).

    Article  Google Scholar 

  16. Roderick, M. L., Greve, P. & Farquhar, G. D. On the assessment of aridity with changes in atmospheric CO2. Water Resour. Res. 51, 5450–5463 (2015).

    Article  CAS  Google Scholar 

  17. Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nat. Clim. Change 9, 44 (2019).

    Article  Google Scholar 

  18. Zhou, S., Yu, B., Lintner, B. R., Findell, K. L. & Zhang, Y. Projected increase in global runoff dominated by land surface changes. Nat. Clim. Chang. 13, 442–449 (2023).

    Article  CAS  Google Scholar 

  19. Scheff, J., Mankin, J. S., Coats, S. & Liu, H. CO2-plant effects do not account for the gap between dryness indices and projected dryness impacts in CMIP6 or CMIP5. Environ. Res. Lett. 16, 034018 (2021).

    Article  CAS  Google Scholar 

  20. Zhou, S. et al. Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands. Nat. Clim. Chang. 11, 38–44 (2021).

    Article  Google Scholar 

  21. Zhou, S. et al. Diminishing seasonality of subtropical water availability in a warmer world dominated by soil moisture–atmosphere feedbacks. Nat. Commun. 13, 5756 (2022).

    Article  CAS  Google Scholar 

  22. Berg, A. et al. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Change 6, 869–874 (2016).

    Article  Google Scholar 

  23. Seneviratne, S. I. et al. Impact of soil moisture–climate feedbacks on CMIP5 projections: first results from the GLACE-CMIP5 experiment. Geophys. Res. Lett. 40, 5212–5217 (2013).

    Article  Google Scholar 

  24. Zhou, S. et al. Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl Acad. Sci. USA 116, 18848–18853 (2019).

    Article  CAS  Google Scholar 

  25. Zhao, L. et al. Evapotranspiration estimation methods in hydrological models. J. Geogr. Sci. 23, 359–369 (2013).

    Article  Google Scholar 

  26. Zhang, K., Kimball, J. S. & Running, S. W. A review of remote sensing based actual evapotranspiration estimation. WIREs Water 3, 834–853 (2016).

    Article  Google Scholar 

  27. Kim, Y., Garcia, M. & Johnson, M. S. Land–atmosphere coupling constrains increases to potential evaporation in a warming climate: implications at local and global scales. Earth’s Future 11, e2022EF002886 (2023).

    Article  Google Scholar 

  28. Zhang, Y. et al. Future global streamflow declines are probably more severe than previously estimated. Nat. Water 1, 261–271 (2023).

    Article  Google Scholar 

  29. Yang, H. & Yang, D. Derivation of climate elasticity of runoff to assess the effects of climate change on annual runoff: derivation of climate elasticity of runoff. Water Resour. Res. 47, W07526 (2011).

    Article  Google Scholar 

  30. Zhou, S. et al. A new method to partition climate and catchment effect on the mean annual runoff based on the Budyko complementary relationship. Water Resour. Res. 52, 7163–7177 (2016).

    Article  Google Scholar 

  31. Shuttleworth, W. J. In Handbook of Hydrology (McGraw-Hill Education, 1993).

  32. Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements (Food and Agriculture Organization of the United Nations, 1998).

  33. Zhou, S. & Yu, B. Physical basis of the potential evapotranspiration and its estimation over land. J. Hydrol. 641, 131825 (2024).

    Article  Google Scholar 

  34. Zhou, S. & Yu, B. Reconciling the discrepancy in projected global dryland expansion in a warming world. Glob. Change Biol. 31, e70102 (2025).

    Article  CAS  Google Scholar 

  35. Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).

    Article  Google Scholar 

  36. Mankin, J. S., Seager, R., Smerdon, J. E., Cook, B. I. & Williams, A. P. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nat. Geosci. 12, 983–988 (2019).

    Article  CAS  Google Scholar 

  37. Brutsaert, W. A generalized complementary principle with physical constraints for land‐surface evaporation. Water Resour. Res. 51, 8087–8093 (2015).

    Article  Google Scholar 

  38. Szilagyi, J. On the inherent asymmetric nature of the complementary relationship of evaporation. Geophys. Res. Lett. 34, L02405 (2007).

    Article  Google Scholar 

  39. Pastorello, G. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).

    Article  Google Scholar 

  40. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  41. Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun. 9, 1094 (2018).

    Article  Google Scholar 

  42. Priestley, C. H. B. & Taylor, R. J. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev. 100, 81–92 (1972).

    Article  Google Scholar 

  43. McColl, K. A., Roderick, M. L., Berg, A. & Scheff, J. The terrestrial water cycle in a warming world. Nat. Clim. Chang. 12, 604–606 (2022).

    Article  Google Scholar 

  44. Berg, A. Bridging the gap between simple metrics and model simulations of climate change impacts on land hydrology. Earth’s Future 10, e2022EF003259 (2022).

    Article  Google Scholar 

  45. Sun, R., Hernández, F., Liang, X. & Yuan, H. A calibration framework for high-resolution hydrological models using a multiresolution and heterogeneous strategy. Water Resour. Res. 56, e2019WR026541 (2020).

    Article  Google Scholar 

  46. Pandi, D., Kothandaraman, S. & Kuppusamy, M. Hydrological models: a review. Int. J. Hydrol. Sci. Technol. 12, 223–242 (2021).

    Article  Google Scholar 

  47. Ma, N., Szilagyi, J. & Zhang, Y. Calibration‐free complementary relationship estimates terrestrial evapotranspiration globally. Water Resour. Res. 57, e2021WR029691 (2021).

    Article  Google Scholar 

  48. Betts, R. A. et al. Projected increase in continental runoff due to plant responses to increasing carbon dioxide. Nature 448, 1037–1041 (2007).

    Article  CAS  Google Scholar 

  49. Wei, H. et al. Direct vegetation response to recent CO2 rise shows limited effect on global streamflow. Nat. Commun. 15, 9423 (2024).

    Article  CAS  Google Scholar 

  50. Zhou, S. et al. Large divergence in tropical hydrological projections caused by model spread in vegetation responses to elevated CO2. Earth’s Future 10, e2021EF002457 (2022).

    Article  Google Scholar 

  51. Allen, M. R. & Ingram, W. J. Constraints on future changes in climate and the hydrologic cycle. Nature 419, 224–232 (2002).

    Article  CAS  Google Scholar 

  52. Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2, 232–250 (2021).

    Article  Google Scholar 

  53. Berg, A. & McColl, K. A. No projected global drylands expansion under greenhouse warming. Nat. Clim. Chang. 11, 331–337 (2021).

    Article  Google Scholar 

  54. Wells, N., Goddard, S. & Hayes, M. J. A self-calibrating palmer drought severity index. J. Clim. 17, 2335–2351 (2004).

    Article  Google Scholar 

  55. Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).

    Article  Google Scholar 

  56. Free vector and raster map data. Natural Earth http://www.naturalearthdata.com (2024).

  57. Chow, V. T., Maidment, D. R. & Mays, L. W. Applied Hydrology (McGraw-Hill Book Company, 1988).

  58. Penman, H. L. Natural evaporation from open water, bare soil and grass. Proc. R. Soc. Lond. Series A 192, 120–145 (1948).

  59. Qualls, R. J. & Crago, R. D. Graphical interpretation of wet surface evaporation equations. Water Resour. Res. 56, e2019WR026766 (2020).

    Article  Google Scholar 

  60. Yang, H., Yang, D., Lei, Z. & Sun, F. New analytical derivation of the mean annual water-energy balance equation. Water Resour. Res. 44, W03410 (2008).

    Article  Google Scholar 

  61. Padrón, R. S., Gudmundsson, L., Greve, P. & Seneviratne, S. I. Large-scale controls of the surface water balance over land: insights from a systematic review and meta-analysis: review of surface water balance controls. Water Resour. Res. 53, 9659–9678 (2017).

    Article  Google Scholar 

  62. Xu, X., Liu, W., Scanlon, B. R., Zhang, L. & Pan, M. Local and global factors controlling water–energy balances within the Budyko framework. Geophys. Res. Lett. 40, 6123–6129 (2013).

    Article  Google Scholar 

  63. Lemordant, L., Gentine, P., Swann, A. S., Cook, B. I. & Scheff, J. Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2. Proc. Natl Acad. Sci. USA 115, 4093–4098 (2018).

    Article  CAS  Google Scholar 

  64. Zhou, S. Neglecting land–atmosphere feedbacks overestimates climate-driven increases in evapotranspiration. Zenodo https://doi.org/10.5281/zenodo.16730362 (2025).

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Acknowledgements

We acknowledge all the principal investigators who contributed data to the Fluxnet2015 dataset (Supplementary Table 1). We also acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 2) for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work was supported by the National Natural Science Foundation of China (grant nos. 42471108 and 42521001, S.Z.) and the National Key Research and Development Program of China (grant no. 2022YFF0801303, S.Z.).

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Authors and Affiliations

  1. State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Sha Zhou

  2. Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Sha Zhou

  3. School of Engineering and Built Environment, Griffith University Nathan Campus, Brisbane, Queensland, Australia

    Bofu Yu

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  1. Sha Zhou
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Contributions

S.Z. conceived of and designed the study. S.Z. and B.Y. contributed to theretical and data analysis and interpretation. S.Z. drafted the paper. S.Z. and B.Y. revised and finalized the paper.

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Correspondence to Sha Zhou.

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Extended data

Extended Data Fig. 1 Illustration of atmospheric and surface constraints on ET and PET (PETe and PETa).

a, ET is constrained by the net radiation (\({R}_{n}\)) and surface moisture availability over a dry surface. b, PETa is constrained by aerodynamic conditions, assuming unlimited water and energy supply. The dry surface in a is hypothetically converted to a wet surface but remains decoupled from the atmosphere, meaning that surface temperature (\({T}_{s}\)), air temperature (\({T}_{a}\)), and vapor pressure (\({e}_{a}\)) remain unchanged from the dry surface. The only change is that the vapor pressure at the evaporative surface becomes saturated (\({e}_{s}^{* }\) replaces unsaturated \({e}_{s}\)). Consequently, sensible heat (\(H\)) over the hypothetical wet surface remains unchanged compared to the dry surface, but the latent heat (\(\lambda {PE}{T}_{a}\)) is much larger than \(\lambda {ET}\). c, PETe is constrained by the net radiation (\({R}_{n}\)), when the entire dry surface becomes saturated and fully coupled with the atmosphere. Both surface and atmospheric conditions cool down (\({T}_{s}^{{\prime} }\) and \({T}_{a}^{{\prime} }\)) and become more humid (\({e}_{s}^{* {\prime} }\) and \({e}_{a}^{{\prime} }\)) relative to the dry environment. As a result, the latent heat (\(\lambda {PE}{T}_{e}\)) increases, while the sensible heat (\({H}_{w}\)) decreases, all constrained by \({R}_{n}\).

Extended Data Fig. 2 Variations in the wet Bowen ratio (βw) from wet to the hypothetically driest conditions.

a, Illustration of the difference in \({\beta }_{w}\) when an ocean grid cell were artificially dried up completely. The grey curve shows the saturation vapor pressure (\({e}^{* }/\gamma\)) as a function of temperature (\(T\)), while the two dashed lines represent air and surface isenthapic processes with constant enthalpy, \(T+e/\gamma\), from wet to the driest conditions. Temperature and vapor pressure for the air and surface are shown in blue for the wet state, pink for the dry state, and purple for the driest state. b, Illustration of changes in \({\beta }_{w}\) when surface and air temperatures change proportionally from wet to the driest conditions for the ocean grid cell. The x-axis refers to the mean surface and air temperature. The blue line shows \({\beta }_{{pt}}\) inferred from the Priestley-Taylor equation (PETpt) for the same ocean grid cell. c-d, Frequency distribution of the sensitivities of \({\beta }_{w}\) and \({\beta }_{{pt}}\) to mean temperature, that is, the ratio of the differences in \({\beta }_{w}\) and \({\beta }_{{pt}}\) over the difference in mean temperature (\(d{\beta }_{w}/{dT}\) and \(d{\beta }_{{pt}}/{dT}\)) between wet and the driest conditions, for ocean grid cells in 2020 and 2100, based on 32 CMIP6 models. Mean sensitivities of \({\beta }_{w}\) and \({\beta }_{{pt}}\) across all grid cells are shown in parentheses.

Extended Data Fig. 3 Sensitivity of PETe and PETa to surface moisture and vegetation changes.

a,b, Frequency distribution of the relative changes in PETe (\(\Delta\)PETe/PETe, a) and PETa (\(\Delta\)PETa/PETa, b) from wet to the driest conditions in 2020 and 2100, based on 32 CMIP6 models. PETe and PETa under wet conditions are derived from ocean grid cells, while their values under the driest conditions are estimated based on the constraint of moist enthalpy (Methods and Extended Data Fig. 2). Mean relative changes across all grid cells and models are shown in each panel. c,d, Frequency distribution of the relative changes in PETe (\(\Delta\)PETe/PETe, c) and PETa (\(\Delta\)PETa/PETa, d) induced by the vegetation physiological responses to elevated CO2 across land grid cells, based on 7 ESMs. In each ESM, relative changes are computed as the ratio of the mean difference between 1pctCO2 and 1pctCO2-rad to the value in 1pctCO2-rad, averaged over the final 30-year period of their 140-year simulations (1960–1989).

Extended Data Fig. 4 Vegetation physiological effects on PETe and PETa between 1pctCO2 and 1pctCO2-rad.

a-c, Multi-model mean PETe from 1pctCO2 and 1pctCO2-rad and their differences (\(\Delta\)PETe) induced by vegetation physiological responses to elevated CO2 during the final 30 years of the 140-year simulations (1960–1989). d-f, The same as a-c, but for PETa. Basemap in a-f is from Natural Earth56.

Extended Data Fig. 5 Consistency between the dryness index and the ET ratio in 1pctCO2-rad.

a, Relationship between the dryness index (PET/P) and the ET ratio (ET/P), using PET from the energy balance (PETe), the Priestley-Taylor (PETpt), the open-water Penman (PETow), the reference crop Penman-Monteith (PETrc), and the energy-only (PETeo) equations. The circles show global terrestrial mean values from 1850 to 1989 (140 years) averaged over the 7 models in 1pctCO2-rad. b, Distribution of the correlation coefficient (r) between global terrestrial mean PET/P and ET/P over the 140 years across the 7 models. The top and bottom of each box represent the first and third quartiles, the center line the median, the whiskers the data range (excluding outliers, shown as circles), and the cross the mean values across the 7 models. c, Changes in the dryness index (PET/P) and the ET ratio over land relative to the first 30-year period during the 140-year simulations (1850-1989). d, Changes in the global mean ET (\(\Delta\)ET) projected by the 7 models and estimated using the Budyko framework with 30-year mean precipitation and PETe (\(\Delta\)ET_PETe), the three Penman equations (\(\Delta\)ET_PETpt, \(\Delta\)ET_PETow, and \(\Delta\)ET_PETrc), and the energy-only equation (\(\Delta\)ET_PETeo). The first year of each 30-year period (111 moving averages in total) is shown along the horizontal axis. The solid lines show 7-model means and the shading refers to one standard deviation across the 7 models in c and d.

Extended Data Fig. 6 ET projections and attribution using the PET based on land-atmosphere coupling (PETLAC).

a, Relationship between the dryness index (PETLAC/P) and the ET ratio (ET/P) with climate change. The circles represent global terrestrial mean values from 1850 to 1989, averaged across 7 models in 1pctCO2-rad. b, Global pattern of the 7-model mean correlation coefficient (r) between annual ET/P and annual PETLAC/P in 1pctCO2-rad. c, Global pattern of the difference in ET changes projected directly from the 7 models (\(\Delta\)ET) and indirectly using the Budyko framework with PETLAC and precipitation (\(\Delta\)ET_Budyko). The 7-model mean \(\Delta\)ET is computed between the first (1850-1879) and final (1960-1989) 30-year periods of 1pctCO2-rad. d, Climate-driven changes in the global mean \(\Delta\)ET projected by the 7 models and estimated using the Budyko framework (\(\Delta\)ET_Budyko) over the 140-year simulation of 1pctCO2-rad. e, Changes in the global mean \(\Delta\)ET projected by the 7 models in 1pctCO2 and the isolated contributions of climate change (\(\Delta\)ETcc, 1pctCO2-rad) and land surface changes (\(\Delta\)ETlsc, 1pctCO2 – 1pctCO2-rad). The climate change effect (\(\Delta\)ETcc_Budyko) and land surface effect (\(\Delta\)ETlsc_Budyko) estimated using the Budyko framework are also shown. The first year of each 30-year period (111 moving averages in total) is shown along the horizontal axis. The solid lines represent the 7-model means and the shading refers to one standard deviation across the 7 models in d and e. Basemap in b and c made with Natural Earth56.

Extended Data Fig. 7 Differences between the potential and actual evaporation over the ocean based on 32 CMIP6 models.

a-c, Multi-model mean differences between the energy-based PETe and ET during the historical period (1980-2009), the future period (2071-2100), and their changes (future minus historical). d-f, The same as a-c, but for the differences between the PET based on land atmosphere coupling (PETLAC) and ET. Grid cells where the absolute difference between PET and ET is less than 0.05 mm/day are shown in grey. Each panel reports the mean and one standard deviation of the PET-ET difference (bias) across all ocean grid cells. Basemap in a-f made with Natural Earth56.

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Zhou, S., Yu, B. Neglecting land–atmosphere feedbacks overestimates climate-driven increases in evapotranspiration. Nat. Clim. Chang. 15, 1099–1106 (2025). https://doi.org/10.1038/s41558-025-02428-5

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