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Dryland dominance in the slowdown of global vegetation carbon uptake

Nature Geoscience (2026)Cite this article

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Abstract

Rising atmospheric CO2 concentrations, temperature and vapour pressure deficit substantially influence plant photosynthesis and terrestrial carbon uptake, yet how these drivers interact to alter photosynthesis across different climate regimes remains unclear. Here, using globally distributed FLUXNET measurements and satellite-derived machine learning estimates of gross primary production (GPP) for 1982–2022, we reveal an asymmetric shift in vegetation productivity between drylands and humid regions. This shift is led by a substantial slowdown in the rate of increase in dryland GPP since 2001, primarily due to water constraints associated with the rising vapour pressure deficit. By contrast, humid regions exhibit a sustained increase in GPP in response to rising temperatures and atmospheric CO2. Notably, dynamic global vegetation models and Earth system models fail to capture this divergence in both historical simulations and future projections. Given increasing atmospheric aridity and the continued expansion of drylands, we anticipate a broad water constraint on global photosynthetic capacity that may limit the land carbon sink. Consequently, we advocate prioritizing adaptive strategies in drylands and nature-based solutions in humid regions to enhance global climate action.

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Fig. 1: Asymmetric rates of change in GPP between global drylands and humid regions during 1982–2000 and 2001–2022.
Fig. 2: Interactions among environmental factors and their influences on GPP in drylands and humid regions during the period 1982–2022.
Fig. 3: Trends in estimated GPP for global drylands and humid regions across historical periods (1982–2000 and 2001–2022), and projected future periods (2041–2060 and 2081–2100), together with the expanding spatial extent of drylands under the SSP5-8.5 scenario.

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Data availability

All data that support the findings of this study are publicly available. Global monthly gridded ET and GPP products (1982–2022) are available as part of the ML- and FLUXNET-based Carbon and Water Fluxes (MF-CW) datasets from the Global Ecology Group Data Repository at https://globalecology.unh.edu/data/MF-CW_v2.html. Additional third-party datasets are available from their original sources, including FLUXNET EC measurements, satellite observations and climate re-analysis products cited in the Methods. The source data underlying Figs. 1–3, Extended Data Table 1, Extended Data Figs. 1–9 and Supplementary Figs. 1–4 are available via Zenodo at https://doi.org/10.5281/zenodo.18476284 (ref. 92).

Code availability

MATLAB (R2024a) code used for data processing, model training and figure generation is available via Zenodo at https://doi.org/10.5281/zenodo.18476284 (ref. 92).

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Acknowledgements

This project was supported by the National Natural Science Foundation of China (42471426), the Science and Technology Program of the Inner Mongolia Autonomous Region (2025YFDZ0055) and the Science and Technology Breakthrough Project of the Inner Mongolia Autonomous Region (2025KJTW0026). J.X. was supported by the US National Science Foundation (NSF) (Macrosystem Biology and NEON-Enabled Science program: DEB-2017870) and Google. A.B. was supported by NASA and USDA. J.P. was supported by the Catalan Government (AGAUR2023 and CLIMA00118). We thank the global FLUXNET community and the AmeriFlux, ICOS, USCCC, ChinaFlux and OzFlux networks for providing EC flux measurements of carbon and water.

Author information

Authors and Affiliations

  1. Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot, China

    Fei Li, Shichao Tian & Xinmiao Hu

  2. Key Laboratory of Grassland and Agricultural Ecological Remote Sensing, Ministry of Agriculture and Rural Affairs, Hohhot, China

    Fei Li

  3. Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA

    Jingfeng Xiao

  4. Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, USA

    Jiquan Chen

  5. Department of Ecosystem and Conservation Science, University of Montana, Missoula, MT, USA

    Ashley Ballantyne

  6. Laboratoire des Sciences du Climat et de l’Environnement, Université Paris-Saclay, Gif-sur-Yvette, France

    Ashley Ballantyne

  7. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain

    Josep Peñuelas

  8. CREAF, Cerdanyola del Vallès, Barcelona, Spain

    Josep Peñuelas

  9. Department of Environmental Science, University of Arizona, Tucson, AZ, USA

    Julia K. Green

  10. College of Grassland Science and Technology, China Agricultural University, Beijing, China

    Yingjun Zhang

  11. Spark Climate Solutions, San Francisco, CA, USA

    Benjamin Poulter

  12. Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK

    Stephen Sitch

  13. College of Resources and Environment, Yangtze University, Wuhan, China

    Jiming Jin

  14. College of Geographical Science, Inner Mongolia Normal University, Hohhot, China

    Gang Bao

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Contributions

F.L. conceived the study and conducted model simulations. F.L., J.X. and A.B. contributed to the study design, results analysis and text drafting. J.C., J.P., J.K.G., Y.Z., B.P. and S.S. contributed to results interpretation and text editing. S.T. contributed to data analysis. J.J., X.H. and G.B. contributed to the compilation and processing of global FLUXNET data and precipitation products.

Corresponding authors

Correspondence to Fei Li or Jingfeng Xiao.

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The authors declare no competing interests.

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Nature Geoscience thanks Gerbrand Koren and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Alison Hunt and Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Spatial trends in ensemble-mean GPP and its relationships with CO2-temperature-VPD interaction factors across two periods.

a,b, Spatial patterns of trends in ensemble-mean GPP derived from 24 machine-learning (ML) estimates for 1982–2000 (a) and 2001–2022 (b). c, Interannual anomalies in GPP and the z-score-normalized indices (Ta×Ca/VPD) and (Ta/VPD) across global vegetated land for 1982–2022. d, Linear relationships between GPP anomalies and (Ta×Ca/VPD) and (Ta/VPD) for 1982–2000 and 2001–2022. Basemaps in a and b from Natural Earth (https://www.naturalearthdata.com).

Extended Data Fig. 2 PLS-SEM analysis of atmospheric CO2 (Ca) and climatic effects on GPP across two periods.

a–c, Partial least squares structural equation modelling (PLS-SEM) of Ca and climatic drivers of GPP for 1982–2000 and 2001–2022 at the global scale (a), in drylands (b) and in humid regions (c). The PLS-SEM considers the influences of climatic drivers and dependence between the explanatory variables (Ta and VPD). Solid lines represent positive effects, whereas dashed lines represent negative effects. Statistical significance of the standardized path coefficients (rₚ) is indicated as P-value < 0.001 (***), P-value < 0.01 (**), and P-value < 0.05 (*).

Extended Data Fig. 3 PET–GPP relationships across two periods in humid regions and drylands.

a,b, PET–GPP relationships for 1982–2000 and 2001–2022 in humid regions (a) and drylands (b). Correlations were assessed using Pearson’s correlation (two-sided), with P-value < 0.05 considered statistically significant.

Extended Data Fig. 4 Trends in precipitation (P) and PET anomalies in drylands and humid regions.

a,b, Trends in P and PET anomalies over global drylands (a) and humid regions (b) from 1982–2022. Trend significance was assessed using a two-sided t-test (P-value < 0.05).

Extended Data Fig. 5 Soil-water–GPP trend relationships and precipitation controls on mean GPP and GPP trends across two periods.

a,b, Relationships between binned soil water (SW) and GPP trends, calculated as grid-cell area-weighted averages, for 1982–2000 (a) and 2001–2022 (b). c,d, Relationships between mean annual precipitation (MAP) and mean annual GPP, and between MAP and GPP trends, for 1982–2000 (c) and 2001–2022 (d), stratified by aridity index (AI) bins (0–5). Basemaps in a and b from Natural Earth (https://www.naturalearthdata.com).

Extended Data Fig. 6 Interaction-index anomalies associated with GPP anomalies in TRENDY DGVMs and ESM-projected GPP responses to rising temperature.

a,b, Anomalies in the z-score-normalized (Ta×Ca/VPD) index accounting for GPP anomalies during 1982–2022, aggregated across 20 TRENDY v12 dynamic global vegetation models (DGVMs) for global drylands (a) and humid regions (b). c,d, Responses of GPP projected by six CMIP6 Earth system models (ESMs) to rising air temperature (Ta) in global drylands (c) and humid regions (d) during 2015–2100 under the SSP5-8.5 scenario.

Extended Data Fig. 7 Projected trends in aridity and hydroclimate variables in drylands and humid regions under SSP5-8.5.

a,b, Trends in aridity index (AI), precipitation (P) and PET anomalies for global drylands (a) and humid regions (b) from 2015–2100 under the CMIP6 SSP5-8.5 scenario. Trend significance was assessed using a two-sided t-test (P-value < 0.05).

Extended Data Fig. 8 Spatial distribution of global drylands across historical and future periods.

a–d, Spatial distribution of global drylands for the historical periods 1982–2000 (a) and 2001–2022 (b) and the future periods 2041–2060 (c) and 2081–2100 (d), based on ERA5 reanalysis data and six CMIP6 ESMs. Basemaps from Natural Earth (https://www.naturalearthdata.com).

Extended Data Fig. 9 Spatial patterns of decreasing soil moisture (SM) and corresponding histograms of area-percentage changes for the periods 1982–2000 and 2001–2022.

a,b, SM data were derived from microwave satellite observations (a) and from GRACE/GRACE-FO data assimilation (b). Basemaps from Natural Earth (https://www.naturalearthdata.com).

Extended Data Table 1 Mean and total GPP and ET across model ensembles
Full size table

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Li, F., Xiao, J., Chen, J. et al. Dryland dominance in the slowdown of global vegetation carbon uptake. Nat. Geosci. (2026). https://doi.org/10.1038/s41561-026-01957-8

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