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Lagged precipitation effects on plant production across terrestrial biomes

Nature Ecology & Evolution volume 9, pages 1800–1811 (2025)Cite this article

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Abstract

Precipitation effects on plant carbon uptake extend beyond immediate timeframes, reflecting temporal lags between rainfall and plant growth. Mechanisms and relative importance of such lagged effects are expected to vary across ecosystems. Here we draw on an extensive collections of productivity proxies from long-term ground measurements, satellite observations and model simulations to show that preceding-year precipitation exerts a comparable influence on plant productivity to current-year precipitation. Statistically supported lagged precipitation effects are detected in 13.4%–19.7% of the grids depending on the data source. In these sites, preceding-year precipitation positively controls current-year plant productivity in water-limited areas, while negative effects occur in some wet regions, such as tropical forests. While aridity emerges as the main driver of this spatial variability, machine learning-based spatial attribution also indicates interactions among plant traits, climatic conditions and soil properties. We also show that soil water dynamics, plant phenology and foliar structure might mediate lagged precipitation effects across time. Our findings highlight the role of preceding-year precipitation in global plant productivity.

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Fig. 1: Comparison of effects of current-year and antecedent precipitation on plant productivity.
Fig. 2: LPEs on plant productivity inferred from ground-based measurements.
Fig. 3: LPEs on plant productivity inferred from satellite observations and model simulations.
Fig. 4: Spatial attribution and temporal mechanisms of LPEs.

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Data availability

All data used in this study are freely available from the following sources: in situ raw tree-ring-width data are sourced from ITRDB (https://www1.ncdc.noaa.gov/pub/data/paleo/treering/measurements). FLUXNET2015 GPP is available from https://fluxnet.org/data/fluxnet2015-dataset. Grassland ANPP is available via Figshare at https://doi.org/10.6084/m9.figshare.13020695.v1 (ref. 103) and https://doi.org/10.5061/dryad.7sqv9s4vv (ref. 104). MODIS EVI and NDVI are available from https://lpdaac.usgs.gov/products/mod13c2v061/. GOSIF SIF is available from https://data.globalecology.unh.edu/data/GOSIF_v2. CSIF SIF is available from https://doi.org/10.11888/Ecolo.tpdc.271751. Ku VOD is available via Zenodo at https://zenodo.org/records/2575599#.Y5nMg3ZBz9A (ref. 105). Merged-band (C, X, Ku) VOD is available from https://doi.org/10.48436/t74ty-tcx62 (ref. 106). GLASS GPP is available from http://www.glass.umd.edu/Download.html. GOSIF GPP is available from https://data.globalecology.unh.edu/data/GOSIF-GPP_v2. MUSES GPP is available from https://zenodo.org/records/3996814 (ref. 107). MODIS-algorithm GPP is available from https://doi.org/10.1038/nclimate2879. TL-LUE GPP is available via Dryad at https://doi.org/10.5061/dryad.dfn2z352k (ref. 108). PML GPP is available via Figshare at https://doi.org/10.6084/m9.figshare.14185739.v4 (ref. 60). LRF GPP is available from https://doi.org/10.17894/ucph.b2d7ebfb-c69c-4c97-bee7-562edde5ce66. MF-CW GPP is available from https://globalecology.unh.edu/data/MF-CW.html. ERA5-Land data are available from https://doi.org/10.24381/cds.e2161bac. MSWX climate data are available from https://www.gloh2o.org/mswx. MSWEP precipitation is available from https://www.gloh2o.org/mswep. CRU precipitation available from https://data.ceda.ac.uk/badc/cru/data/cru_ts/cru_ts_4.08/data. CPC precipitation is available from https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. GPCC precipitation is available from https://doi.org/10.5676/DWD_GPCC/FD_M_V2022_050. Aridity index is available via Figshare at https://doi.org/10.6084/m9.figshare.7504448.v5 (ref. 109). GLEAM soil moisture is available from https://www.gleam.eu. Atmospheric CO2 concentration is available from https://gml.noaa.gov/ccgg/trends. Terrestrial water storage is available via Figshare at https://doi.org/10.6084/m9.figshare.7670849 (ref. 110). SoilGrids data are available from https://www.isric.org/explore/soilgrids. Soil phosphorus concentration is available via Figshare at https://doi.org/10.6084/m9.figshare.14241854 (ref. 111). GTOPO30 elevation is available from https://doi.org/10.5066/F7DF6PQS. GIMMS LAI 4 g is available via Zenodo at https://doi.org/10.5281/zenodo.7649107 (ref. 112). Plant species richness is available from https://doi.org/10.7910/DVN/PDNWKL. Maximum root depth is available from https://doi.org/10.1073/pnas.1712381114. Forest age is available from https://doi.org/10.17871/ForestAgeBGI.2021. Tree density is available from https://elischolar.library.yale.edu/yale_fes_data/1/. Biome type is available from https://ecoregions.appspot.com. The field RWI, BAI and related climate data are also available via Zenodo at https://doi.org/10.5281/zenodo.15313896 (ref. 113). Source data are provided with this paper.

Code availability

All data analyses were performed using R (v.4.4.2). The code is available via Zenodo at https://doi.org/10.5281/zenodo.15313896 (ref. 113).

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Acknowledgements

This study was supported by National Natural Science Foundation of China grant no. 41921001. J.W. was funded by the ‘Kezhen and Bingwei’ Young Scientist Programme of IGSNRR. D.M.P.P was supported by NSF-DEB grant no. 2213599. J.X. was supported by the University of New Hampshire via bridge support and the Iola Hubbard Climate Change Endowment.

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Authors and Affiliations

  1. State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China

    Lei He & Zhao-Liang Li

  2. The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Jian Wang

  3. University of the Chinese Academy of Sciences, Beijing, China

    Jian Wang

  4. School of Life Sciences, University of Nevada-Las Vegas, Las Vegas, NV, USA

    Drew M. P. Peltier

  5. Laboratoire des Sciences du Climat et de l’Environnement, CEA/CNRS/UVSQ/ Université Paris Saclay, Gif-sur-Yvette, France

    François Ritter & Philippe Ciais

  6. CSIC, Global Ecology CREAF-CSIC-UAB, Cerdanyola del Vallès, Barcelona, Spain

    Josep Peñuelas

  7. CREAF, Cerdanyola del Vallès, Barcelona, Spain

    Josep Peñuelas

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

    Jingfeng Xiao

  9. Institute of Integrative Biology, ETH Zurich (Swiss Federal Institute of Technology), Zurich, Switzerland

    Thomas W. Crowther

  10. School of Geography and Planning, Sun Yat-sen University, Guangzhou, China

    Xing Li

  11. State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, China

    Jian-Sheng Ye

  12. Graduate School of Environment and Information Science, Yokohama National University, Yokohama, Japan

    Takehiro Sasaki

  13. Center for Ocean Remote Sensing of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, China

    Chenghu Zhou

  14. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Zhao-Liang Li

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  1. Lei He
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Contributions

J.W., Z.-L.L. and L.H. designed this study. L.H. performed the analyses and visualization. L.H. and J.W. wrote the first draft of the paper. D.M.P.P., F.R. and P.C. substantially revised the paper with intensive suggestions. All authors discussed the results and contributed to the revisions.

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Correspondence to Jian Wang or Zhao-Liang Li.

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He, L., Wang, J., Peltier, D.M.P. et al. Lagged precipitation effects on plant production across terrestrial biomes. Nat Ecol Evol 9, 1800–1811 (2025). https://doi.org/10.1038/s41559-025-02806-4

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