Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Future socio-ecosystem productivity threatened by compound drought–heatwave events

Nature Sustainability volume 6, pages 259–272 (2023)Cite this article

Subjects

Abstract

Compound drought–heatwave (CDHW) events are one of the worst climatic stressors for global sustainable development. However, the physical mechanisms behind CDHWs and their impacts on socio-ecosystem productivity remain poorly understood. Here, using simulations from a large climate–hydrology model ensemble of 111 members, we demonstrate that the frequency of extreme CDHWs is projected to increase by tenfold globally under the highest emissions scenario, along with a disproportionate negative impact on vegetation and socio-economic productivity by the late twenty-first century. By combining satellite observations, field measurements and reanalysis, we show that terrestrial water storage and temperature are negatively coupled, probably driven by similar atmospheric conditions (for example, water vapour deficit and energy demand). Limits on water availability are likely to play a more important role in constraining the terrestrial carbon sink than temperature extremes, and over 90% of the global population and gross domestic product could be exposed to increasing CDHW risks in the future, with more severe impacts in poorer and more rural areas. Our results provide crucial insights towards assessing and mitigating adverse effects of compound hazards on ecosystems and human well-being.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Anomalies of composite water–heat–carbon variables during extreme climatic events.
Fig. 2: Recent changes in frequency and intensity of CDHWs as well as related socio-economic exposure.
Fig. 3: Anomalies of water, heat and carbon fluxes due to concurrent hot–drought conditions during historical and future periods as estimated by climate models.
Fig. 4: Future changes in the characteristics of CDHWs and socio-economic exposure to CDHWs under model simulations.
Fig. 5: Projected JRP of historical 50-year bivariate CDHWs and socio-economic exposure.

Data availability

The CMIP5-based TWS simulations are freely available from the ISIMIP project portal (https://data.isimip.org/search/tree/ISIMIP2b/InputData/climate/). The three GRACE/GRACE-FO products are available from http://www2.csr.utexas.edu/grace/, https://grace.jpl.nasa.gov/data/get-data/ and https://earth.gsfc.nasa.gov. The long-term reconstructed TWS data are available on Figshare (https://doi.org/10.6084/m9.figshare.7670849). The TWS simulations under CMIP6 are available at the repository in the Open Science Framework (https://osf.io/hy96r/); this dataset cannot be accessed now, because the data are in an embargo period and currently shared only among the ISIMIP participants. The SPEI dataset is available at https://spei.csic.es/database.html. The GLDAS-2.2 data are available at https://ldas.gsfc.nasa.gov/gldas/forcing-data. The ERA5 reanalysis data are from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. The GLEAM 3.5a data are from https://www.gleam.eu/. The FLUXNET2015 dataset is from https://fluxnet.org/data/fluxnet2015-dataset/. The gridded SIF dataset is from https://doi.org/10.17605/OSF.IO/8XQY6, and the gridded GPP dataset is available from https://data.tpdc.ac.cn/en/data/582663f5-3be7-4f26-bc45-b56a3c4fc3b7/. The global gridded population data are available from https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals-rev11; the global gridded GDP data and the GDP per capita data are available from https://datadryad.org/stash/dataset/doi:10.5061/dryad.dk1j0. The BEST dataset is available at Berkeley Earth (http://berkeleyearth.org/data/).

Code availability

The R (version 4.1.0) code used for producing Figs. 1–5 and the MATLAB (version 2020a) code used for data analysis are available at the repository in the Open Science Framework (https://osf.io/dnuxv/).

References

  1. Zhang, P. et al. Abrupt shift to hotter and drier climate over inner East Asia beyond the tipping point. Science 370, 1095–1099 (2020).

    Article  Google Scholar 

  2. Jaeger, W. K. et al. Scope and limitations of drought management within complex human–natural systems. Nat. Sustain. 2, 710–717 (2019).

    Article  Google Scholar 

  3. Lim, E. P. et al. Australian hot and dry extremes induced by weakenings of the stratospheric polar vortex. Nat. Geosci. 12, 896–901 (2019).

    Article  CAS  Google Scholar 

  4. Bevacqua, E. et al. Precipitation trends determine future occurrences of compound hot–dry events. Nat. Clim. Change 12, 350–355 (2022).

    Article  Google Scholar 

  5. Dirmeyer, P. A., Jin, Y., Singh, B. & Yan, X. Evolving land–atmosphere interactions over North America from CMIP5 simulations. J. Clim. 26, 7313–7327 (2013).

    Article  Google Scholar 

  6. Alizadeh, M. R. et al. A century of observations reveals increasing likelihood of continental-scale compound dry–hot extremes. Sci. Adv. 6, eaaz4571 (2020).

    Article  Google Scholar 

  7. Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).

    Article  CAS  Google Scholar 

  8. Gampe, D. et al. Increasing impact of warm droughts on northern ecosystem productivity over recent decades. Nat. Clim. Change 11, 772–779 (2021).

    Article  Google Scholar 

  9. Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Change 3, 292–297 (2013).

    Article  Google Scholar 

  10. Anderegg, W. R. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).

    Article  CAS  Google Scholar 

  11. Zampieri, M., Ceglar, A., Dentener, F. & Toreti, A. Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environ. Res. Lett. 12, 064008 (2017).

    Article  Google Scholar 

  12. Guerreiro, S. B. et al. Future heat-waves, droughts and floods in 571 European cities. Environ. Res. Lett. 13, 034009 (2018).

    Article  Google Scholar 

  13. Keenan, T. F. et al. A constraint on historic growth in global photosynthesis due to increasing CO2. Nature 600, 253–258 (2021).

    Article  CAS  Google Scholar 

  14. Noon, M. L. et al. Mapping the irrecoverable carbon in Earth’s ecosystems. Nat. Sustain. 5, 37–46 (2022).

    Article  Google Scholar 

  15. Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).

    Article  CAS  Google Scholar 

  16. Ruehr, N. K., Grote, R., Mayr, S. & Arneth, A. Beyond the extreme: recovery of carbon and water relations in woody plants following heat and drought stress. Tree Physiol. 39, 1285–1299 (2019).

    Article  CAS  Google Scholar 

  17. Lobell, D. B., Deines, J. M. & Tommaso, S. D. Changes in the drought sensitivity of US maize yields. Nat. Food 1, 729–735 (2020).

    Article  Google Scholar 

  18. Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).

    Article  CAS  Google Scholar 

  19. 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 

  20. Mazdiyasni, O. & AghaKouchak, A. Substantial increase in concurrent droughts and heatwaves in the United States. Proc. Natl Acad. Sci. USA 112, 11484–11489 (2015).

    Article  CAS  Google Scholar 

  21. Zscheischler, J. et al. A typology of compound weather and climate events. Nat. Rev. Earth Environ. 1, 333–347 (2020).

    Article  Google Scholar 

  22. Naumann, G. et al. Increased economic drought impacts in Europe with anthropogenic warming. Nat. Clim. Change 11, 485–491 (2021).

    Article  Google Scholar 

  23. Satoh, Y. et al. A quantitative evaluation of the issue of drought definition: a source of disagreement in future drought assessments. Environ. Res. Lett. 16, 104001 (2021).

    Article  Google Scholar 

  24. Mukherjee, S. & Mishra, A. K. Increase in compound drought and heatwaves in a warming world. Geophys. Res. Lett. 48, e2020GL090617 (2021).

    Article  Google Scholar 

  25. Zhao, M. et al. Ecological restoration impact on total terrestrial water storage. Nat. Sustain. 4, 56–62 (2021).

    Article  Google Scholar 

  26. Pokhrel, Y. et al. Global terrestrial water storage and drought severity under climate change. Nat. Clim. Change 11, 226–233 (2021).

    Article  Google Scholar 

  27. Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosci 15, 5779–5800 (2018).

    Article  CAS  Google Scholar 

  28. Zhang, Y. et al. A global moderate resolution dataset of gross primary production of vegetation for 2000–2016. Sci. Data 4, 170165 (2017).

    Article  Google Scholar 

  29. 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 

  30. Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).

    Article  Google Scholar 

  31. Drake, J. E. et al. A common thermal niche among geographically diverse populations of the widely distributed tree species Eucalyptus tereticornis: no evidence for adaptation to climate-of-origin. Glob. Change Biol. 23, 5069–5082 (2017).

    Article  Google Scholar 

  32. Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).

    Article  CAS  Google Scholar 

  33. Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).

    Article  CAS  Google Scholar 

  34. Yin, J. et al. Projection of droughts and their socioeconomic exposures based on terrestrial water storage anomaly over China. Sci. China Earth Sci. 65, 1772–1787 (2022).

    Article  Google Scholar 

  35. Campbell, J. E. et al. Large historical growth in global terrestrial gross primary production. Nature 544, 84–87 (2017).

    Article  CAS  Google Scholar 

  36. Wang, X. et al. Emergent constraint on crop yield response to warmer temperature from field experiments. Nat. Sustain. 3, 908–916 (2020).

    Article  Google Scholar 

  37. Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 129 (2015).

    Article  Google Scholar 

  38. Rodrigues, R. R. & Woollings, T. Impact of atmospheric blocking on South America in austral summer. J. Clim. 30, 1821–1837 (2017).

    Article  Google Scholar 

  39. Büntgen, U. et al. Recent European drought extremes beyond Common Era background variability. Nat. Geosci. 14, 190–196 (2021).

    Article  Google Scholar 

  40. Schumacher, D. L. et al. Amplification of mega-heatwaves through heat torrents fuelled by upwind drought. Nat. Geosci. 12, 712–717 (2019).

    Article  CAS  Google Scholar 

  41. Hallegatte, S. et al. Poverty and climate change: introduction. Environ. Dev. Econ. 23, 217–233 (2018).

    Article  Google Scholar 

  42. Fankhauser, S. & McDermott, T. K. J. Understanding the adaptation deficit: why are poor countries more vulnerable to climate events than rich countries? Glob. Environ. Change 27, 9–18 (2014).

    Article  Google Scholar 

  43. Burke, M., Hsiang, S. & Miguel, E. Global non-linear effect of temperature on economic production. Nature 527, 235–239 (2015).

    Article  CAS  Google Scholar 

  44. Elkouk, A. et al. Implications of changes in climate and human development on 21st-century global drought risk. J. Environ. Manage. 317, 115378 (2022).

    Article  Google Scholar 

  45. Lange, S. Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset. Earth Syst. Dyn. 9, 627–645 (2018).

    Article  Google Scholar 

  46. Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). Geosci. Model Dev. 12, 3055–3070 (2019).

    Article  Google Scholar 

  47. Humphrey, V. & Gudmundsson, L. GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth Syst. Sci. Data 11, 1153–1170 (2019).

    Article  Google Scholar 

  48. Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol. 11, 1424–1439 (2005).

    Article  Google Scholar 

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

    Article  Google Scholar 

  50. Kummu, M. et al. Gridded global datasets for gross domestic product and Human Development Index over 1990–2015. Sci. Data 5, 180004 (2018).

    Article  Google Scholar 

  51. Jing, C. et al. Population, urbanization and economic scenarios over the Belt and Road region under the Shared Socioeconomic Pathways. J. Geogr. Sci. 30, 68–84 (2020).

    Article  Google Scholar 

  52. Koutsoyiannis, D. Clausius–Clapeyron equation and saturation vapor pressure: simple theory reconciled with practice. Eur. J. Phys. 33, 295–305 (2012).

    Article  Google Scholar 

  53. Simmons, A. J., Untch, A., Jakob, C., Kallberg, P. & Unden, P. Stratospheric water vapor and tropical tropopause temperatures in ECMWF analyses and multi-year simulations. Q. J. R. Meteorol. Soc. 125, 353–386 (1999).

    Article  Google Scholar 

  54. Zhao, M., Velicogna, I. & Kimball, J. S. Satellite observations of regional drought severity in the continental United States using GRACE-based terrestrial water storage changes. J. Clim. 30, 6297–6308 (2017).

    Article  Google Scholar 

  55. Keellings, D. & Waylen, P. Increased risk of heat waves in Florida: characterizing changes in bivariate heat wave risk using extreme value analysis. Appl. Geogr. 46, 90–97 (2014).

    Article  Google Scholar 

  56. Yin, J. et al. Global increases in lethal compound heat stress: hydrological drought hazards under climate change. Geophys. Res. Lett. 49, e2022GL100880 (2022).

    Article  Google Scholar 

  57. Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Contr. 19, 716–723 (1974).

    Article  Google Scholar 

  58. Zscheischler, J. & Seneviratne, S. I. Dependence of drivers affects risks associated with compound events. Sci. Adv. 3, e1700263 (2017).

    Article  Google Scholar 

  59. Salvadori, G. et al. A multivariate copula-based framework for dealing with hazard scenarios and failure probabilities. Water Resour. Res. 52, 3701–3721 (2016).

    Article  Google Scholar 

  60. Chegwidden, O. S. et al. How do modeling decisions affect the spread among hydrologic climate change projections? Exploring a large ensemble of simulations across a diversity of hydroclimates. Earth’s Future 7, 623–637 (2019).

    Article  Google Scholar 

Download references

Acknowledgements

J.Y. acknowledges support from the National Natural Science Foundation of China (grant no. 52009091). L.S. acknowledges support from UK Research and Innovation (grant no. MR/V022008/1). Y.P. acknowledges support from the National Science Foundation (CAREER Award, grant no. 1752729). J.Y. is also supported by the Fundamental Research Funds for the Central Universities (grant no. 2042022kf1221). The numerical calculations in this paper were performed on the supercomputing system in the Supercomputing Center of Wuhan University.

Author information

Authors and Affiliations

  1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China

    Jiabo Yin, Shenglian Guo & Lihua Xiong

  2. Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA

    Pierre Gentine

  3. Earth Institute, Columbia University, New York, NY, USA

    Pierre Gentine

  4. School of Geography and the Environment, University of Oxford, Oxford, UK

    Louise Slater

  5. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China

    Lei Gu

  6. Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI, USA

    Yadu Pokhrel

  7. Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan

    Naota Hanasaki

  8. School of International and Public Affairs, Columbia University, New York, NY, USA

    Wolfram Schlenker

Authors
  1. Jiabo Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pierre Gentine
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Louise Slater
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yadu Pokhrel
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Naota Hanasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shenglian Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lihua Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wolfram Schlenker
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.Y. conceived and designed the study. J.Y. processed the model simulations and reanalysis data. N.H. conducted the H08 simulations. J.Y., P.G., L.S., L.G., Y.P., S.G., L.X. and W.S. contributed to the data analysis and interpretation. J.Y. drafted the manuscript. All authors edited the manuscript.

Corresponding author

Correspondence to Jiabo Yin.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Sustainability thanks William Jaeger and Sourav Mukherjee for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Relationship between daily maximum near-surface temperature (Tmax) and terrestrial water storage (TWS) or root-zone soil moisture (SM) during 2002–2020.

a-d, Pearson’s correlation coefficient between: monthly GRACE/GRACE-FO ensemble mean TWS and ERA5 Tmax (a), monthly reconstructed TWS and Tmax from Berkeley Earth Surface Temperatures (b), daily ERA5 SM and Tmax (c), daily GLEAM SM and ERA5 Tmax (d). Insets in a-d show the histogram of the correlation coefficient, with the dashed vertical line representing the median value. The graph on the right of each panel shows the latitudinal median. e-h, Mean probability of each percentile bin across all land grid cells (excluding Greenland and Antarctica in all analyses).

Extended Data Fig. 2 Coupling of Tmax and monthly TWS from the three GRACE/GRACE-FO solutions dataset and their impacts on terrestrial carbon uptake.

a-c, Probability of each percentile bin of Tmax and monthly TWS across 73 flux tower sites. d-f, Mean anomalies of GPP for each percentile bin of Tmax and TWS. g-i, Mean anomalies of TER for each percentile bin of Tmax and TWS. j-l, Mean anomalies of NEP for each percentile bin of Tmax and TWS. The three columns represent the GRACE/GRACE-FO TWS data produced from JPL, CSR and GSFC, respectively. At each site, anomalies of GPP, TER, and NEP are calculated as the difference between the daily values in extreme events and the mean daily values in the warm season (defined as days when running 7-day mean temperatures are higher than the 60th percentile of daily temperature for the site).

Extended Data Fig. 3 Anomalies of SIF and GPP during extreme climatic events.

a, b, Anomalies of SIF (a) and GPP (b) during extreme heat events. c-d, Anomalies of SIF (c) and GPP (d) during extreme dry events. e, f, Anomalies of SIF (e) and GPP (f) in concurrent heat and dry conditions. At each grid, anomalies of SIF (GPP) are calculated as the difference between the 4-day (8-day) values in extreme events and the mean 4-day (8-day) values in the warm season. Dry conditions are identified using GRACE/GRACE-FO ensemble mean TWS data, and the heat conditions are identified by ERA5 Tmax. Insets show the histogram of the anomalies, with the dashed vertical line representing the median value. The graph on the right shows the latitudinal median value.

Extended Data Fig. 4 Temporal dynamics of the GDP exposures to CDHW in 21 Giorgi climate regions.

Each panel has a cluster of 21 grey lines, which show the ensemble of the regional GDP exposures in all regions. The black line in each figure represents the exposure value in each region, and the color lines represent trends of GDP exposures during recent, past and entire periods. The droughts are identified by reconstructed TWS data, and the heatwaves are detected by using Tmax from the BEST dataset. The * indicates the trend is significant (p < 0.05) detected by Mann-Kendall test.

Extended Data Fig. 5 Anomalies of GPP, TER and NEP due to extreme heat or drought conditions in the GFDL-CLM4.5 model.

a-i, Anomalies of carbon fluxes in the historical period (a-c), RCP 2.6 (d-f) and RCP 6.0 (g-i) due to extreme heat conditions (monthly Tmax above the 90th percentile). j-r, Anomalies of carbon fluxes in the historical period (j-l), RCP2.6 (m-o) and RCP6.0 (p-r) due to droughts (TWS-DSI < −0.8). The graph on the right shows the latitudinal median and 90% confidence interval. The TWS and carbon fluxes are projected by CLM4.5 model with bias-corrected GFDL-GCM2M outputs.

Extended Data Fig. 6 Future changes in characteristics of CDHW and heatwaves.

Insets in each figure show the histogram of the relative change percentages, with the dashed vertical line representing the mean value. Stippling denotes regions where the sign of the relative changes is consistent with the sign of the multi-model means (as shown in the figure) in at least 80% of GCM-THM models. These results are derived from the ISIMIP2b multiple impacts model ensemble.

Extended Data Fig. 7 Future changes in the characteristics of CDHW and socioeconomic exposure to CDHW under CMIP6.

a-l, Relative changes in the frequency (a-c), average duration (d-f), average severity (g-i) and coincidence rate (j-l) of CDHW from the historical to the future periods. m-p, Temporal dynamics of the global average coincidence rate (m), exposed land area (n), exposed population (o) and exposed GDP (p). Insets in a-l show the histogram of the relative change percentages, with the dashed vertical line representing the mean value. Stippling in a-l denotes regions where the sign of the relative changes is consistent with the sign of the multi-model means (as shown in the figure) in at least 80% of GCMs. In m-p, the shading represents ±1 standard deviation, and only the historical exposures linking to SSP126 TWS data are presented. For projecting CDHW, the TWS is simulated by driving H08 forced by five bias-corrected GCMs under CMIP6.

Extended Data Fig. 8 Projected JRP of historical 50-year bivariate CDHW and socioeconomic exposure under CMIP6.

a-c, Average JRP in the future period under a non-stationary bivariate framework. d-i, Population (d-f) and GDP (g-i) exposure due to increasing risk of bivariate CDHW in the future period. j-l, Temporal dynamics of the global average exposed land area (j), population (k) and GDP (l) due to increasing CDHW risk; the solid curve and shading indicate multi-model mean ± SD. m, Boxplot of updated JRP of the historical 50-year CDHW in different Giorgi climate regions under SSP585; the centre line indicates median value, and the box bounds (whiskers) indicate 25th/75th percentile (min/max) values. n, Average contribution ratios of seven uncertainty sources in different Giorgi climate regions and in the global landmass (Glob). Stippling in a-i denotes regions where the sign of the JRP is consistent with the sign of the multi-model means (as shown in the figure) in at least 80% of GCMs. For projecting CDHW, the TWS is simulated by driving H08 forced by five bias-corrected GCMs under CMIP6.

Extended Data Fig. 9 CDHW coincidence rate and socioeconomic exposures to CDHW in rich versus poor areas.

a-f, Temporal dynamics of the global average coincidence rate (a, b), and exposed GDP fraction (c, d) and population fraction (e, f) to CDHW. g-i, Average coincidence rate (g), GDP exposure fraction (h) and population exposure fraction (i) during 2070–2099 in different Giorgi climate regions under SSP585. In a-f, the shading represents ±1 standard deviation, and only the historical exposures linking to SSP126 TWS data are presented. For projecting CDHW, the TWS is simulated by driving H08 forced by five bias-corrected GCMs under CMIP6. Rich (poor) areas are identified where the 2015-year GDP per capita exceeds (is below) the 80th (20th) percentile values in different regions.

Extended Data Fig. 10 Gridded Gross Domestic Product (GDP) per capita (purchasing power parity) in constant 2011 international US dollars (USD) for six typical years during 1990–2015.

a-f, GDP per capita in year of 1990 (a), 1995 (b), 2000 (c), 2005 (d), 2010 (e) and 2015 (f).

Supplementary information

Supplementary Information

Supplementary Figs. 1–30, Texts 1 and 2, and Tables 1–3.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yin, J., Gentine, P., Slater, L. et al. Future socio-ecosystem productivity threatened by compound drought–heatwave events. Nat Sustain 6, 259–272 (2023). https://doi.org/10.1038/s41893-022-01024-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41893-022-01024-1

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing