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Global warming amplifies wildfire health burden and reshapes inequality

Nature volume 647, pages 928–934 (2025)Cite this article

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Abstract

Global warming intensifies wildfires and exacerbates greenhouse-gas and pollutant emissions1. However, global projections remain incomplete, hindering effective policy interventions amid uncertain warming scenarios2. Here we developed an interpretable machine learning framework to project global burned areas and wildfire emissions. This framework accounts for the effects of future climate change on fire activity and quantifies the associated number of premature deaths and radiative forcing from fire-induced particulate matter (fine particulate matter less than 2.5 μm in diameter (PM2.5)). Here we show that from 2010–2014 to 2095–2099, fire carbon emissions are projected to increase by 23% under Shared Socio-economic Pathway 2-4.5. Increased fire-related aerosols reduce the 0.06 W m−2 cooling effect north of 60° N. Projections show a surge in the number of premature deaths from wildfire smoke, reaching 1.40 (95% confidence interval = 0.66–2.25) million people annually during 2095–2099—roughly six times higher than current levels. Africa is projected to experience the greatest rise in fire-related deaths (11-fold), driven by emission changes and an ageing population. Europe and the USA would experience a one to twofold increase under Shared Socio-economic Pathway 2-4.5, linked to rising fire occurrences in the mid-latitude Northern Hemisphere. Overall, the health burden would become more evenly distributed across nations of differing development levels than present patterns, underscoring the need for coordinated efforts.

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Fig. 1: The historical and projected future fire emissions and their effects on the number of PM2.5-related premature deaths and aerosol radiative forcing.
Fig. 2: Spatial distributions of PM2.5-induced premature deaths and the all-sky aerosol DRF at TOA from fire emissions.
Fig. 3: Global numbers of premature deaths attributed to fire-induced PM2.5 exposure based on different sensitivity experiments.
Fig. 4: Regional numbers of premature deaths attributed to fire-induced PM2.5 exposure and their inequality related to GDP for the historical and future periods.

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Data availability

Output for the CMIP6 model is publicly available through the Earth System Grid Federation at https://esgf-node.llnl.gov/projects/cmip6. The European Centre for Medium-Range Weather Forecasts Reanalysis v.5 data can be accessed via the Copernicus Climate Data Store at https://cds.climate.copernicus.eu. GCAP2.0 meteorological data are available at http://atmos.earth.rochester.edu/input/gc/ExtData/. LUH2 land-use data are available at https://aims2.llnl.gov/search/input4mips. The 2021 GBD air pollution exposure estimates at 0.1° × 0.1° resolution are available from https://ghdx.healthdata.org/record/ihme-data/gbd-2021-air-pollution-exposure-estimates-1990-2021. The projected fire emissions constructed in this study can be obtained from https://doi.org/10.5281/zenodo.16926891 (ref. 80). Further datasets supporting the main findings of this study are described in the main text, the Methods and the Supplementary Information. Source data are provided with this paper.

Code availability

The GEOS-Chem v13.2.0 model code is available at https://doi.org/10.5281/zenodo.5500536 (ref. 81). The other analysis codes used in this study are available at https://doi.org/10.5281/zenodo.16927100 (ref. 82).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant nos. 42375096 and 22188102) and the Carbon Neutrality and Energy System Transformation (CNEST) programme. Q.Z. acknowledges the support by the New Cornerstone Science Foundation through the Xplorer Prize. P.C. acknowledges the support from the CALIPSO project funded through the generosity of Schmidt Science and of the ESA RECCAP2-CS project 4000144908/24/I-LR.

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Authors and Affiliations

  1. Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Junri Zhao, Bo Zheng & Longyi Zhang

  2. State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China

    Bo Zheng

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

    Philippe Ciais & Thomas Gasser

  4. Department of Earth System Science, University of California, Irvine, Irvine, CA, USA

    Yang Chen

  5. International Institute for Applied Systems Analysis (IIASA), Schlossplatz, Laxenburg, Austria

    Thomas Gasser

  6. CSIRO Environment, Canberra, Australian Capital Territory, Australia

    Josep G. Canadell

  7. Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China

    Qiang Zhang

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Contributions

B.Z. and Q.Z. conceived, designed and supervised the study, and secured funding. J.Z. developed the emission estimation method, conducted the model simulations, performed data analysis and generated the figures. B.Z. and J.Z. wrote the original draft. B.Z., P.C., Y.C., T.G., J.G.C., L.Z, and Q.Z. reviewed and edited the manuscript.

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Correspondence to Bo Zheng or Qiang Zhang.

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Extended data figures and tables

Extended Data Fig. 1 Global evaluation of burned area and fire carbon emissions from GFEDv4.1 s, PRD, and ESMs from 2000 to 2014.

(a) Temporal trend comparison of the global burned area from GFEDv4.1 s (BA) (purple curve) and the predicted values in this study (PRD (BA), green curve) alongside their respective fitted linear trends (dashed lines). The error bars in PRD (BA) represent the min-max range in prediction based on four ESMs. The asterisks indicate whether the trend is statistically significant (Mann–Kendall test; **P < 0.01). (b) Spatial distribution of mean fire carbon emissions from GFEDv4.1 s averaged from 2000 to 2014, and the disparities with predictions from PRD (c) and ESMs (d) compared to GFEDv4.1 s. In the parentheses, BA represents burned area, and C represents fire carbon emissions.

Source data

Extended Data Fig. 2 Spatial distributions of trends of fire carbon emissions from 2015 to 2099.

(a) and (b) represent trends of fire carbon emissions in our projection estimates under SSP2-4.5 and SSP5-8.5 scenarios, respectively. (c) and (d) represent the projection results from ESMs. The trends in each grid cell are shown using the linear least squares fitting method based on annual time series. (e–h) depict the corresponding latitudinal trends. Statistically significant trends (p < 0.05) are indicated by asterisks (∗∗) in latitudinal trends.

Source data

Extended Data Fig. 3 Latitudinal variations of fire carbon emissions.

(a) Annual mean fire carbon emissions for the historical period (2010–2014). (b) The changes in fire carbon emissions of the late 21st century (2095–2099) under the SSP2-4.5 scenario (green bar) and the SSP5-8.5 scenario (red bar) compared to historical estimates during 2010–2014.

Source data

Extended Data Table 1 Description of sensitivity experiments for future PM2.5-related premature mortality assessment
Full size table
Extended Data Table 2 The global premature deaths resulting from fire-induced PM2.5 exposure
Full size table

Supplementary information

Source data

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Zhao, J., Zheng, B., Ciais, P. et al. Global warming amplifies wildfire health burden and reshapes inequality. Nature 647, 928–934 (2025). https://doi.org/10.1038/s41586-025-09612-9

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