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Increase in plant reliance on past precipitation associated with greening and drying

Nature Ecology & Evolution (2026)Cite this article

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Abstract

Water is indispensable for life on Earth. Plants use water either from recent precipitation (within a month) or from past precipitation stored in deeper soil (PP; at least a month ago) to maintain metabolism and growth. It is widely known that plants tend to rely more on PP to buffer against short-term rainfall deficits. However, how this reliance has changed in response to global change remains unclear. Here we develop a novel framework to estimate temporal changes in plant reliance on PP during the past four decades. Observational data reveal that 42% of tropical and subtropical natural ecosystems have experienced a significant increase in plant reliance on PP over the period 1982–2021 (P < 0.05). Such an increase is consistent with greening during the late growing season in drylands and drying during the wet-to-dry transitional period in non-drylands, when short-term precipitation fails to meet plant water demand. Adaptive changes in root depth and species composition may further facilitate this change in PP reliance, especially in drylands. Our study highlights the importance of PP in ecosystem functioning and implies an increasing ecosystem resilience to climate variability.

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Fig. 1: Spatial patterns of plant reliance on PP.
Fig. 2: Seasonal changes of \({{\boldsymbol{\beta }}}_{{{\bf{P}}}_{{\bf{P}}}}\).
Fig. 3: Trend of annual \({{\boldsymbol{\beta }}}_{{{\bf{P}}}_{{\bf{P}}}}\) and its associating factors.
Fig. 4: Trend of annual \({{\boldsymbol{\beta }}}_{{{\bf{P}}}_{{\bf{P}}}}\) in the trend of the LGS NDVI and WDTP PR − EP domain.

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Data availability

GLEAM v3.8 can be requested from the GLEAM team (info@gleam.eu), GLEAM4 is available at https://www.gleam.eu/, the X-BASE data are available at https://doi.org/10.18160/5NZG-JMJE, the GLDAS-Noah data are available at https://doi.org/10.5067/9SQ1B3ZXP2C5, the in situ transpiration data are available from J. Nelson (jnelson@bgc-jena.mpg.de) upon reasonable request, the TRENDYv6 data are available from S. Sitch (s.a.sitch@exeter.ac.uk) upon reasonable request, both the isotope-based and the inverse modelling-based estimates are available at http://thredds-gfnl.usc.es/thredds/catalog/DATA_TRANSPSOURCES/catalog.html, the MSWEP precipitation is available at www.gloh2o.org, the CRU climate dataset is available at https://crudata.uea.ac.uk/cru/data/hrg/, the GPCC precipitation data are available at https://www.dwd.de/EN/ourservices/gpcc/gpcc.html, the PKU GIMMS NDVI dataset is available at https://doi.org/10.5281/zenodo.8253971 (ref. 96), the ISLSCP II MODIS IGBP Land Cover is available at http://daac.ornl.gov, the tree cover data are available at https://doi.org/10.5067/MEASURES/VCF/VCF5KYR.001, the cropland area in 2019 is available at https://glad.umd.edu/dataset/croplands, the Global-AI_PET_v3 is available at https://doi.org/10.6084/m9.figshare.7504448.v5 (ref. 97), documented cases of woody encroachment are available at https://doi.org/10.5281/zenodo.3601454 (ref. 98) and the reconstructed TWSA are available at https://doi.org/10.6084/m9.figshare.7670849 (ref. 99). Source data are provided with this paper.

Code availability

Data analysis was performed in R 4.4.1 and Python 3.9.7, and all the figures were generated using R 4.1.1. All code used in the study is available via figshare at https://doi.org/10.6084/m9.figshare.30919049 (ref. 100).

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Acknowledgements

This study is supported by the National Natural Science Foundation of China (42141005). D.G.M. acknowledges support from the European Research Council Consolidator Grant HEAT (101088405). X.T. acknowledges support from the US National Science Foundation grants 2442269 and 2106030. H.Y. acknowledges support from the National Natural Science Foundation of China (grant number 42401104). This work is supported by the High-Performance Computing Platform of Peking University. We thank the TRENDY v12 project for providing model simulations to support our analysis. We appreciate J. Nelson for providing the transpiration estimates at flux tower sites.

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Authors and Affiliations

  1. Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Hongying Zhang, Yao Zhang & Hui Yang

  2. Hydro-Climate Extremes Lab, Ghent University, Ghent, Belgium

    Diego G. Miralles

  3. Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ, USA

    Xiaonan Tai

  4. Department of Environmental Science and Technology, University of Maryland, College Park, MD, USA

    Akash Koppa

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

    Stephen Sitch & Michael O’Sullivan

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Contributions

Y.Z. conceived the idea, Y.Z. and H.Z. designed the study, H.Z. performed the analysis, and Y.Z. and H.Z. wrote the first draft of the paper. All co-authors commented on the results and contributed to the writing of the paper.

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Correspondence to Yao Zhang.

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Nature Ecology & Evolution thanks Ying Fan, Taylor Smith and Yuting Yang for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Spatial patterns of monthly \({{\boldsymbol{\beta }}}_{{{\bf{P}}}_{{\bf{P}}}}\).

Spatial patterns of \({\beta }_{{P}_{P}}\) in January (a), February (b), March (c), April (d), May (e), June (f), July (g), August (h), September (i), October (j), November (k), and December (l) during 2001–2015.

Extended Data Fig. 2 Temporal dynamics of monthly \({{\boldsymbol{\beta }}}_{{{\bf{P}}}_{{\bf{P}}}}\), PR, and EP.

Average monthly \({\beta }_{{\rm{P}}_{\rm{P}}}\), PR, and EP across Köppen–Geiger climate types (a‒i). Brown hollow squares, red lines, and blue bars represent \({\beta }_{{P}_{P}}\), \({E}_{P}\), and \({P}_{R}\), respectively, across 9 major climate types during 2001–2015. Brown error bars indicate ±1 standard deviation of \({\beta }_{{P}_{P}}\) within each climate regions. Data for the Southern Hemisphere are shifted by six months. The proportional area covered by each major climate type in the study regions is annotated above the corresponding subplot.

Extended Data Fig. 3 Seasonal changes of PR −EP and NDVI.

Monthly \({P}_{R}-{E}_{P}\) and NDVI during 1982 − 2021 in the two-dimensional space, with the reordered months of the year as x-axis and aridity index as y-axis. The months were reordered by starting with the wettest months indicated by \({P}_{R}-{E}_{P}\) (a), and the greenest months, indicated by NDVI (b). The late growing season is marked with white slashes, which starts with the month with maximum NDVI and ends when it decreases to half of the amplitude. The wet-to-dry transitional period is marked with white backslashes, which starts with the month with maximum cumulative \({P}_{R}-{E}_{P}\) and lasts for 3 months.

Extended Data Fig. 4 Hotspot regions of trends in annual \({{\boldsymbol{\beta }}}_{{{\bf{P}}}_{{\bf{P}}}}\), WDTP PR −EP, and LGS NDVI.

Trends of annual \({\beta }_{{P}_{P}}\) and WDTP \({P}_{R}-{E}_{P}\) in non-drylands (a, c), annual \({\beta }_{{P}_{P}}\) and LGS NDVI in drylands (b, d) from 1982 to 2021. Hotspots are defined as regions where the absolute values of \({\beta }_{{P}_{P}}\) trends exceed the 60th percentile, calculated separately for drylands and non-drylands.

Extended Data Fig. 5 Differences between NDVI and PR −EP in 2000‒2021 and 1982‒1999.

The results were presented in the two-dimensional space, with the reordered months of the year as x-axis and aridity levels as y-axis. The months were reordered by starting with the greenest months, indicated by NDVI (a‒b), and the wettest months indicated by \({P}_{R}-{E}_{P}\) (c‒d). The late growing season is marked with white slashes, while the wet-to-dry transitional period is marked with white backslashes.

Extended Data Fig. 6 Comparison between \({{\boldsymbol{\beta }}}_{{{\bf{P}}}_{{\bf{P}}}}\) and its trend calculated from GLEAM4 and DGVMs outputs.

Dry and wet season mean \({\beta }_{{P}_{P}}\) during 2001‒2015, and trends in annual \({\beta }_{{P}_{P}}\) from 1982 to 2015 derived using 10-year moving windows. Dry and wet seasons were defined according to monthly mean \({P}_{R}\) and \({E}_{P}\) during 2001‒2015. Wet season is the period with monthly mean \({P}_{R}\) exceeding \({E}_{P}\); and the rest is dry season. For results based on GLEAM4, \({P}_{R}\) is from MSWEP, and \({E}_{P}\) is from GLEAM4; For results based on DGVMs outputs, both \({P}_{R}\) and \({E}_{P}\) are from CRU TS v4.07. Median values in drylands (marked as circles) and non-drylands (marked as rectangles) are illustrated with colors.

Extended Data Fig. 7 Vertical distribution of fine root biomass in response to climate change.

Climate change factors include elevated CO2 (a), warming (b), increased precipitation (c), and decreased precipitation (d). Each point represents the log response ratio (lnRR) for a specific soil layer in a single experiment: shallow (0–20 cm), median (20–50 cm), and deep (>50 cm). Red lines denote shifts toward deeper fine root distributions (higher lnRR in soils below 20 cm), whereas blue lines indicate shifts toward shallower distributions. Grey boxes show the distribution of lnRR values across soil layers.

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Zhang, H., Zhang, Y., Miralles, D.G. et al. Increase in plant reliance on past precipitation associated with greening and drying. Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-026-02997-4

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