Abstract
Heatwaves are becoming more intense and frequent as global temperatures rise, affecting vulnerable populations, particularly in low-income communities. Addressing the impacts of heatwaves requires high-resolution data to assess their influence on labour productivity, public health, and climate risk. We introduce the Comprehensive Heat Indices (CHI) dataset, a high-resolution (0.1° × 0.1°) hourly dataset from 1950 to 2024, derived from the ERA5 and ERA5-Land reanalyses. The CHI dataset encompasses thirteen heat stress indices, including wet-bulb temperature, universal thermal climate index, mean radiant temperature, wind chill, and lethal heat stress index (Ls). Thresholds for Ls are empirically linked to mortality, enabling the identification of life-threatening heat events. Ls is sensitive to soil moisture variability, improving assessments in agricultural regions. The CHI dataset supports indoor and outdoor applications and is sensitive to humidity, radiation, and wind. Covering the global land area from 60°S to 75°N and 180°W to 180°E, it provides a unique, long-term perspective on spatial and temporal trends in heat stress, which are critical for climate impact research and adaptation planning.
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Data availability
The user can freely access the data, along with the user guide, description, and metadata from https://doi.org/10.6084/m9.figshare.30539867.
Code availability
The Python library thermofeel44, used to calculate most of the heat stress indices, is freely available on GitHub at https://github.com/ecmwf/thermofeel. We used thermofeel to compute rh and all heat indices except Tnwb. For Tnwb, along with variables such as cos θ, ws10, ws2, dsrp, and fdir, we utilised the Python code developed by Kong et al.45, which is available at https://zenodo.org/records/5980536. Both the thermofeel44 and Kong et al.45 codes were optimised and adapted to meet the requirements of CHI dataset production. The modified and integrated version of these codes is available for download and further use from the GitHub repository at https://github.com/masabhathini/CHIdatasets.
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Acknowledgements
The present study was funded by the Climate Change Center, King Abdullah University of Science and Technology (KAUST). Muhammad Usman was supported by Zayed University (Research Incentive Fund: RIF 23021), Abu Dhabi, UAE. The authors thank the KAUST Supercomputing Laboratory for providing computing resources. This research used the Shaheen III Supercomputer managed by the Supercomputing Core Laboratory at King Abdullah University of Science & Technology (KAUST). We thank the Climate Data Store (C3S) of the Copernicus Climate Change Service for providing the reanalysis products. This work uses, and may include modifications of, Copernicus Climate Change Service information. Neither the European Commission nor ECMWF is responsible for any use of the Copernicus information or data contained herein.
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A.M. – conceptualisation, data acquisition, methodology, formal analysis, technical validation, writing – original draft; S.M. – code optimisation and development, data management, integration, pre-processing, validation, and storage, writing – review & editing; M.A.S. – code compilation, technical assistance, resources provision, writing – review & editing; Q.K. – code scripting, software, writing – review & editing; M.U. – scientific input, writing – review & editing; H.P.D. – writing – review & editing I.H. – writing – review & editing, supervision, project administration, funding acquisition.
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Malik, A., Masabathini, S., Shaikh, M.A. et al. A Global High-Resolution Comprehensive Heat Indices Dataset from 1950 to 2024. Sci Data (2026). https://doi.org/10.1038/s41597-025-06519-y
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DOI: https://doi.org/10.1038/s41597-025-06519-y
