Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).
Google Scholar
Pendergrass, A. G. & Knutti, R. The uneven nature of daily precipitation and its change. Geophys. Res. Lett. 45, 11,980–11,988 (2018).
Google Scholar
Feldman, A. F. et al. Plant responses to changing rainfall frequency and intensity. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-024-00534-0 (2024).
Google Scholar
Thomey, M. L. et al. Effect of precipitation variability on net primary production and soil respiration in a Chihuahuan Desert grassland. Glob. Change Biol. 17, 1505–1515 (2011).
Google Scholar
Fay, P. A. et al. Relative effects of precipitation variability and warming on tallgrass prairie ecosystem function. Biogeosciences 8, 3053–3068 (2011).
Google Scholar
Liu, J. et al. Impact of temporal precipitation variability on ecosystem productivity. Wiley Interdiscip. Rev. Water 7, e1481 (2020).
Google Scholar
Sloat, L. L. et al. Increasing importance of precipitation variability on global livestock grazing lands. Nat. Clim. Change 8, 214–218 (2018).
Google Scholar
Ritter, F., Berkelhammer, M. & Garcia-Eidell, C. Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability. Commun. Earth Environ. 1, 34 (2020).
Google Scholar
Guan, K. et al. Continental-scale impacts of intra-seasonal rainfall variability on simulated ecosystem responses in Africa. Biogeosciences 11, 6939–6954 (2014).
Google Scholar
Knapp, A. K. et al. Rainfall variability, carbon cycling, and plant species diversity in a mesic grassland. Science 298, 2202–2205 (2002).
Google Scholar
Ross, I. et al. How do variations in the temporal distribution of rainfall events affect ecosystem fluxes in seasonally water-limited Northern Hemisphere shrublands and forests? Biogeosciences 9, 1007–1024 (2012).
Google Scholar
Su, J., Zhang, Y. & Xu, F. Divergent responses of grassland productivity and plant diversity to intra-annual precipitation variability across climate regions: a global synthesis. J. Ecol. 111, 1921–1934 (2023).
Google Scholar
Good, S. P. & Caylor, K. K. Climatological determinants of woody cover in Africa. Proc. Natl Acad. Sci. USA 108, 4902–4907 (2011).
Google Scholar
Zhang, F. et al. Precipitation temporal repackaging into fewer, larger storms delayed seasonal timing of peak photosynthesis in a semi‐arid grassland. Funct. Ecol. 36, 646–658 (2021).
Google Scholar
Xu, X., Medvigy, D. & Rodriguez-Iturbe, I. Relation between rainfall intensity and savanna tree abundance explained by water use strategies. Proc. Natl Acad. Sci USA. 112, 12992–12996 (2015).
Google Scholar
Case, M. F. & Staver, A. C. Soil texture mediates tree responses to rainfall intensity in African savannas. New Phytol. 219, 1363–1372 (2018).
Google Scholar
Heisler-White, J. L., Blair, J. M., Kelly, E. F., Harmoney, K. & Knapp, A. K. Contingent productivity responses to more extreme rainfall regimes across a grassland biome. Glob. Change Biol. 15, 2894–2904 (2009).
Google Scholar
Jasechko, S. et al. Terrestrial water fluxes dominated by transpiration. Nature 496, 347–350 (2013).
Google Scholar
Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).
Google Scholar
Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N. & Huybers, P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nat. Food 1, 127–133 (2020).
Google Scholar
Wang, L. et al. Dryland productivity under a changing climate. Nat. Clim. Change 12, 981–994 (2022).
Google Scholar
Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).
Google Scholar
Gherardi, L. A. & Sala, O. E. Effect of interannual precipitation variability on dryland productivity: a global synthesis. Glob. Change Biol. 25, 269–276 (2019).
Google Scholar
Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).
Google Scholar
Maurer, G. E., Hallmark, A. J., Brown, R. F., Sala, O. E. & Collins, S. L. Sensitivity of primary production to precipitation across the United States. Ecol. Lett. 23, 527–536 (2020).
Google Scholar
Sala, O. E., Parton, W. J., Joyce, L. A. & Lauenroth, W. K. Primary production of the central grassland region of the United States. Ecology 69, 40–45 (1988).
Google Scholar
Biederman, J. A. et al. CO2 exchange and evapotranspiration across dryland ecosystems of southwestern North America. Glob. Change Biol. 23, 4204–4221 (2017).
Google Scholar
Ukkola, A. M. et al. Annual precipitation explains variability in dryland vegetation greenness globally but not locally. Glob. Change Biol. 27, 4367–4380 (2021).
Google Scholar
Trugman, A. T., Medvigy, D., Mankin, J. S. & Anderegg, W. R. L. Soil moisture stress as a major driver of carbon cycle uncertainty. Geophys. Res. Lett. 45, 6495–6503 (2018).
Google Scholar
Denissen, J. M. C. et al. Widespread shift from ecosystem energy to water limitation with climate change. Nat. Clim. Change 12, 677–684 (2022).
Google Scholar
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).
Google Scholar
Li, F. et al. Global water use efficiency saturation due to increased vapor pressure deficit. Science 381, 672–677 (2023).
Google Scholar
Smith, W. K. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).
Google Scholar
Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).
Google Scholar
Lian, X., Zhao, W. & Gentine, P. Recent global decline in rainfall interception loss due to altered rainfall regimes. Nat. Commun. 13, 7642 (2022).
Google Scholar
Feldman, A. F., Short Gianotti, D. J., Trigo, I. F., Salvucci, G. D. & Entekhabi, D. Land–atmosphere drivers of landscape-scale plant water content loss. Geophys. Res. Lett. 47, e2020GL090331 (2020).
Google Scholar
Feldman, A. F. et al. Moisture pulse-reserve in the soil–plant continuum observed across biomes. Nat. Plants 4, 1026–1033 (2018).
Google Scholar
Williams, C. A., Hanan, N., Scholes, R. J. & Kutsch, W. Complexity in water and carbon dioxide fluxes following rain pulses in an African savanna. Oecologia 161, 469–480 (2009).
Google Scholar
Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).
Google Scholar
Sun, Y. et al. From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I—Harnessing theory. Glob. Change Biol. 29, 2926–2952 (2023).
Google Scholar
Smith, W. K., Fox, A. M., MacBean, N., Moore, D. J. P. & Parazoo, N. C. Constraining estimates of terrestrial carbon uptake: new opportunities using long-term satellite observations and data assimilation. New Phytol. 225, 105–112 (2020).
Google Scholar
Fatichi, S., Ivanov, V. Y. & Caporali, E. Investigating interannual variability of precipitation at the global scale: is there a connection with seasonality? J. Clim. 25, 5512–5523 (2012).
Google Scholar
Knapp, A. K. et al. Consequences of more extreme precipitation regimes for terrestrial ecosystems. Bioscience 58, 811–821 (2008).
Google Scholar
Green, J. K., Berry, J., Ciais, P., Zhang, Y. & Gentine, P. Amazon rainforest photosynthesis increases in response to atmospheric dryness. Sci. Adv. 6, eabb7232 (2020).
Google Scholar
Post, A. K. & Knapp, A. K. Plant growth and aboveground production respond differently to late-season deluges in a semi-arid grassland. Oecologia 191, 673–683 (2019).
Google Scholar
Feldman, A. F., Chulakadabba, A., Short Gianotti, D. J. & Entekhabi, D. Landscape-scale plant water content and carbon flux behavior following moisture pulses: from dryland to mesic environments. Water Resour. Res. 57, e2020WR027592 (2021).
Google Scholar
Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).
Google Scholar
Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).
Google Scholar
Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–900 (2015).
Google Scholar
Pendergrass, A. G. What precipitation is extreme? Science 360, 1072–1073 (2018).
Google Scholar
Kannenberg, S. A., Bowling, D. R. & Anderegg, W. R. L. Hot moments in ecosystem fluxes: high GPP anomalies exert outsized influence on the carbon cycle and are differentially driven by moisture availability across biomes. Environ. Res. Lett. 15, 054004 (2020).
Google Scholar
Wainwright, C. M., Allan, R. P. & Black, E. Consistent trends in dry spell length in recent observations and future projections. Geophys. Res. Lett. 49, e2021GL097231 (2022).
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Google Scholar
Higgins, S. I., Conradi, T. & Muhoko, E. Shifts in vegetation activity of terrestrial ecosystems attributable to climate trends. Nat. Geosci. 16, 147–153 (2023).
Google Scholar
Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 0.05 Deg CMG V061 EarthData https://doi.org/10.5067/MODIS/MOD13C1.061 (2021).
Vermote, E. et al. NOAA Climate Data Record (CDR) of Normalized Difference Vegetation Index (NDVI), Version 4. AVH13C1 (NOAA National Centers for Environmental Information, 2014); https://doi.org/10.7289/V5PZ56R6.
OCO-2-Science-Team, Gunson, M. & Eldering, A. OCO-2 Level 2 Bias-corrected Solar-induced Fluorescence and Other Select Fields from the IMAP-DOAS Algorithm Aggregated as Daily Files, Retrospective Processing V10r (Goddard Earth Sciences Data and Information Services Center, 2020).
Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. 6, 2803–2823 (2013).
Google Scholar
Huffman, G., Stocker, E. F., Bolvin, D. T., Nelkin, E. J. & Tan, J. GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06 (Goddard Earth Sciences Data and Information Services Center, 2019).
Xie, P. et al. A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeorol. 8, 607–626 (2007).
Google Scholar
Contractor, S. et al. Rainfall Estimates on a Gridded Network (REGEN)—a global land-based gridded dataset of daily precipitation from 1950 to 2016. Hydrol. Earth Syst. Sci. 24, 919–943 (2020).
Google Scholar
Roca, R. et al. FROGS: a daily 1° × 1° gridded precipitation database of rain gauge, satellite and reanalysis products. Earth Syst. Sci. Data 11, 1017–1035 (2019).
Google Scholar
Reichle, R. H. et al. Land surface precipitation in MERRA-2. J. Clim. 30, 1643–1664 (2017).
Google Scholar
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).
Google Scholar
Copernicus Climate Change Service Climate Data Store. CMIP6 climate projections. Climate Data Store https://doi.org/10.24381/cds.c866074c (2021).
Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1346 (2018).
Google Scholar
NASA/LARC/SD/ASDC. CERES and GEO-Enhanced TOA, Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Daily Terra-Aqua Edition4A [Data set]. EarthData https://doi.org/10.5067/Terra+Aqua/CERES/SYN1degDay_L3.004A (2017).
Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).
Google Scholar
Wan, Z., Hook, S. & Hulley, G. MYD11C2 MODIS/Aqua Land Surface Temperature/Emissivity 8-Day L3 Global 0.05 Deg CMG V006. EarthData https://doi.org/10.5067/MODIS/MYD11C2.006 (2015).
O’Neill, P. E. et al. SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 3 (NASA National Snow and Ice Data Center, 2019).
Harmonized World Soil Database v2.0 (Food and Agriculture Organization of the United Nations, 2024); https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/.
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).
Google Scholar
Feldman, A. F., Konings, A., Piles, M. & Entekhabi, D. The Multi-Temporal Dual Channel Algorithm (MT-DCA) (Version 5) [Data set]. Zenodo https://doi.org/10.5281/zenodo.5619583 (2021).
Kim, S. Ancillary Data Report: Landcover Classification JPL D-53057 (Jet Propulsion Laboratory, California Institute of Technology, 2013).
Sala, O. E. & Lauenroth, W. K. Small rainfall events: an ecological role in semiarid regions. Oecologia 53, 301–304 (1982).
Google Scholar
Giorgi, F., Raffaele, F. & Coppola, E. The response of precipitation characteristics to global warming from climate projections. Earth Syst. Dyn. 10, 73–89 (2019).
Google Scholar
Grömping, U. Estimators of relative importance in linear regression based on variance decomposition. Am. Stat. 61, 139–147 (2007).
Google Scholar
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).
Google Scholar
Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).
Google Scholar
Lewińska, K. E. et al. Beyond “greening” and “browning”: trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation. Glob. Change Biol. 29, 4620–4637 (2023).
Google Scholar
Ludwig, M., Moreno-Martinez, A., Hölzel, N., Pebesma, E. & Meyer, H. Assessing and improving the transferability of current global spatial prediction models. Glob. Ecol. Biogeogr. 32, 356–368 (2023).
Google Scholar
James, G. M., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R (Springer, 2014).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar
Brunsdon, C., Fotheringham, A. S. & Charlton, M. E. Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr. Anal. 28, 281–298 (1996).
Google Scholar
Li, Y. et al. Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems. Nat. Clim. Change 13, 182–188 (2023).
Google Scholar
Greene, W. H. Econometric Analysis (Prentice Hall, 2003).
Griffin-Nolan, R. J., Slette, I. J. & Knapp, A. K. Deconstructing precipitation variability: rainfall event size and timing uniquely alter ecosystem dynamics. J. Ecol. https://doi.org/10.1080/10643389.2012.728825 (2021).
Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).
Google Scholar
Madani, N., Kimball, J. S., Jones, L. A., Parazoo, N. C. & Guan, K. Global analysis of bioclimatic controls on ecosystem productivity using satellite observations of solar-induced chlorophyll fluorescence. Remote Sens. 9, 530 (2017).
Google Scholar
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In 31st Conference on Neural Information Processing System (NeurIPS, 2017); https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html.
Andrews, T. et al. On the effect of historical SST patterns on radiative feedback. J. Geophys. Res. Atmos. 127, e2022JD036675 (2022).
Bueso, D. et al. Soil and vegetation water content identify the main terrestrial ecosystem changes. Natl. Sci. Rev. 10, nwad026 (2023).
Ives, A. R. et al. Statistical inference for trends in spatiotemporal data. Remote Sens. Environ. 266, 112678 (2021).
Google Scholar
Cortés, J. et al. Where are global vegetation greening and browning trends significant? Geophys. Res. Lett. 48, 1–9 (2021).
Google Scholar
Cortés, J., Mahecha, M., Reichstein, M. & Brenning, A. Accounting for multiple testing in the analysis of spatio-temporal environmental data. Environ. Ecol. Stat. 27, 293–318 (2020).
Google Scholar
Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).
Google Scholar
Feldman, A. Feldman et al. Global one degree datasets. Zenodo https://doi.org/10.5281/zenodo.10947071 (2024).
Feldman, A. et al. Feldman et al. 2024 “Large global scale vegetation sensitivity to daily rainfall variability”. Zenodo https://doi.org/10.5281/zenodo.13551521 (2024).
