Abstract
Rising urban temperatures and heat extremes pose an urgent global challenge, yet the potential for mitigating excessive urban heat–particularly at the global scale–remains unclear. Here, we quantify the cooling potential across 2,265 cities worldwide by the 2050s using validated urban climate simulations. Cooling effects are quantified as the reduction in the summer average wet-bulb globe temperature (WBGT) and heat danger hours (HDH; WBGT > 31.4 °C) under the combined implementation of reflective surfaces, green transformation, and anthropogenic heat reduction. We show a distinct spatial asymmetry: while the cooling potential increases with latitude, primarily due to greater cooling from reflective surfaces, the highest heat risk is concentrated in low- to mid-latitude regions (10°N-40°N). In these high-risk regions, combined mitigation is more effective at night, reducing HDH by an average of 37%, whereas daytime heat is mitigated to a lesser extent (11%). These asymmetries underscore the need for context-specific strategies—particularly accelerated action and localized innovation for low-latitude humid regions—as well as the integration of city-scale planning with targeted daytime heat risk interventions.
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Data availability
The Bias-corrected can be accessed at https://www.scidb.cn/en/detail?dataSetId=791587189614968832. The EC-earth3 dataset are available at https://aims2.llnl.gov/search/cmip6/. The ERA5-Land hourly data are available at https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview. The GHS built-up grid data are available at https://human-settlement.emergency.copernicus.eu/download.php?ds=smod. The weather station data for model validation are available at https:// www.ncei.noaa.gov/access/search/data-search/global-hourly. The original LCZ-based land cover dataset is downloaded from https://zenodo.org/records/7670653. The future land cover projections are available at https://zenodo.org/records/4584775.
Code availability
The Weather Research and Forecasting (WRF) model can be downloaded from https://www.mmm.ucar.edu/models/wrf. The urban fraction and other urban parameters are calculated based on the WUDAPT-to-WRF python tool (https://github.com/matthiasdemuzere/w2w). The code used to generate the figures is available upon reasonable request. All statistical analyses and data visualizations were performed using the open-source Python (version 3.9.15) environment with the following packages: NumPy (v1.23.0), Pandas (v1.5.2), GeoPandas (v0.12.2), Matplotlib (v3.5.3), Cartopy (v0.22.0), PDAL (v2.4.3), PyProj (v3.4.1), W2W (v0.5.0), SciPy (v1.10.1), and Scikit-learn (v1.1.3).
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Acknowledgements
We acknowledge Dominique Derome, Dominik Strebel, Haiwei Li, and Aytaç Kubilay for their valuable discussions during this research. Xiaotian Ding was supported by the Chinese Scholarship Council (No. 202306320335). The research was funded by funding from the National Natural Science Foundation of China (52578147) (Y.F.), “Pioneer” and “Leading Goose” R&D Program of Zhejiang (NO. 2023C03152) (Y.F.) and Zhejiang University-ETH Zürich Global Partnership Fund (No. 100000-11320/209) (Y.F.). The SWEET-SWICE project funded by the Swiss Federal Office of Energy is acknowledged.
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X.D., Y.Z. and Y.F. conceived and designed the study. X.D. conducted the simulations and analysis with the contributions from Y.Z., Y.F. and J.C. X.D. wrote the original draft. Y.Z., Y.F., J.C., D. Ürge-V. and J.G. reviewed and edited the manuscript. All authors approved the final version of the manuscript.
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Ding, X., Fan, Y., Zhao, Y. et al. Asymmetric global urban cooling potential demands accelerated and context-specific actions. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70662-2
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DOI: https://doi.org/10.1038/s41467-026-70662-2
