Abstract
Extreme heat waves (HWs), defined here as periods when daily maximum temperature exceeds the 98th percentile for three or more consecutive days, pose a significant threat to public health. The extreme heat risk is particularly relevant to urban residents due to the urban heat island (UHI) effect and its synergistic interactions with HWs in some cities. Previous research on the interactions between UHI and HWs has focused on a single city or region, and both positive and negative interactions have been reported. The global patterns of interactions between UHI and HWs across various climate backgrounds, as well as their underlying mechanisms, remain unclear. Here, we simulate the global urban heat island intensity (UHII) from 1985 to 2013 using the Community Land Model (CLM). By conducting a global-scale analysis of interactions between UHII and HWs, we diagnose their spatial and diurnal patterns across different regions and climate zones. To identify and explain the key contributors to the UHI-HW interactions, we employ machine learning models (CatBoost) and the SHapley Additive exPlanations (SHAP) framework to quantify the contributions of local energy flux, climate background, and land surface characteristics. UHI-HW interaction is quantified as the difference between UHII during heatwave (HW) days and non-heatwave (NHW) periods. We find that the UHI-HW interaction, which peaks at 6 AM local solar time (LT), is more positive at night than during the day. We identify net longwave radiation as a strong indicator of the interaction, while humidity emerges as a key driver, alongside contributions from sensible heat flux and wind speed. However, the influence of these factors varies across different Köppen–Geiger climate zones. Our study provides new insights into the complex interaction between UHIs and heat waves, with implications for urban climate adaptation strategies in a warming world. The machine learning-based approach offers a novel method for attributing the spatial variability in UHI heat wave interactions to specific biophysical variables.
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References
Roxon, J., Ulm, F. J. & Pellenq, R. J. M. Urban heat island impact on state residential energy cost and CO2 emissions in the United States. Urban Clim. 31, 100546 (2020).
Vicedo-Cabrera, A. M. et al. The burden of heat-related mortality attributable to recent human-induced climate change. Nat. Clim. Chang. 11, 492–500 (2021).
Zander, K. K., Botzen, W. J., Oppermann, E., Kjellstrom, T. & Garnett, S. T. Heat stress causes substantial labour productivity loss in Australia. Nat. Clim. Chang. 5, 647–651 (2015).
Kong, J., Zhao, Y., Carmeliet, J. & Lei, C. Urban heat island and its interaction with heatwaves: A review of studies on mesoscale. Sustainability 13, 10923 (2021).
Cui, F. et al. Interactions between the summer urban heat islands and heat waves in Beijing during 2000–2018. Atmos. Res. 291, 106813 (2023).
Park, K., Baik, J.-J., Jin, H.-G. & Tabassum, A. Changes in urban heat island intensity with background temperature and humidity and their associations with near-surface thermodynamic processes. Urban Clim. 58, 102191 (2024).
Zhao, L. et al. Interactions between urban heat islands and heat waves. Environ. Res. Lett. 13, 034003 (2018).
Khan, H. S., Paolini, R., Santamouris, M. & Caccetta, P. Exploring the synergies between urban overheating and heatwaves (HWs) in western sydney. Energies 13, 470 (2020).
Li, D., Sun, T., Liu, M., Wang, L. & Gao, Z. Changes in wind speed under heat waves enhance urban heat islands in the beijing metropolitan area. J. Appl. Meteorol. Climatol. 55, 2369–2375 (2016).
Shu, C., Gaur, A., Lacasse, M. & Wang, L. L. Interaction between the urban heat island effect and the occurrence of heatwaves: Comparison of days with and without heatwaves, in International Conference on Building Energy and Environment 3019–3028 (Springer, 2022).
Tabassum, A., Park, K., Seo, J. M., Han, J.-Y. & Baik, J.-J. Characteristics of the urban heat island in dhaka, bangladesh, and its interaction with heat waves. Asia-Pac. J. Atmos. Sci. 60, 479–493 (2024).
Li, D. et al. Contrasting responses of urban and rural surface energy budgets to heat waves explain synergies between urban heat islands and heat waves. Environ. Res. Lett. 10, 054009 (2015).
Zhang, K. et al. Increased heat risk in wet climate induced by urban humid heat. Nature 617, 738–742 (2023).
Venter, Z. S., Chakraborty, T. & Lee, X. Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms. Sci. Adv. 7, eabb9569 (2021).
Danabasoglu, G. et al. The community earth system model version 2 (CESM2). J. Adv. Model. Earth Syst. 12, e2019-001916 (2020).
Oleson, K. et al. Technical description of the community land model (clm). NCAR Technical Note (2004).
Lawrence, D. M. et al. The community land model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).
Gao, J. & O’Neill, B. C. Mapping global urban land for the 21st century with data-driven simulations and shared socioeconomic pathways. Nat. Commun. 11, 2302 (2020).
Jackson, T. L., Feddema, J. J., Oleson, K. W., Bonan, G. B. & Bauer, J. T. Parameterization of urban characteristics for global climate modeling. Ann. Assoc. Am. Geogr. 100, 848–865 (2010).
Zhao, L., Lee, X., Smith, R. B. & Oleson, K. Strong contributions of local background climate to urban heat islands. Nature 511, 216–219 (2014).
Lipson, M. J. et al. Evaluation of 30 urban land surface models in the urban-plumber project: Phase 1 results. Q. J. R. Meteorol. Soc. 150, 126–169 (2024).
Oleson, K. & Feddema, J. Parameterization and surface data improvements and new capabilities for the community land model urban (clmu). J. Adv. Model. Earth Syst. 12, e2018MS001586 (2020).
Garuma, G. F. How the interaction of heatwaves and urban heat islands amplify urban warming. Adv. Environ. Eng. Res. 3, 1–1 (2022).
Lee, X. Eq. 2.32, in Fundamentals of Boundary-Layer Meteorology (Springer, 2023).
An, N. et al. An observational case study of synergies between an intense heat wave and the urban heat island in Beijing. J. Appl. Meteorol. Climatol. 59, 605–620 (2020).
Jiang, S., Lee, X., Wang, J. & Wang, K. Amplified urban heat islands during heat wave periods. J. Geophys. Res. Atmos. 124, 7797–7812 (2019).
Park, K., Jin, H.-G. & Baik, J.-J. Contrasting interactions between urban heat islands and heat waves in Seoul, South Korea, and their associations with synoptic patterns. Urban Clim. 49, 101524 (2023).
Chew, L. W., Liu, X., Li, X.-X. & Norford, L. K. Interaction between heat wave and urban heat island: A case study in a tropical coastal city. Singapore Atmos. Res. 247, 105134 (2021).
Kumar, R. & Mishra, V. Decline in surface urban heat island intensity in India during heatwaves. Environ. Res. Commun. 1, 031001 (2019).
Scott, A. A., Waugh, D. W. & Zaitchik, B. F. Reduced Urban Heat Island intensity under warmer conditions. Environ. Res. Lett. 13, 064003 (2018).
Rogers, C. D., Gallant, A. J. & Tapper, N. J. Is the urban heat island exacerbated during heatwaves in southern Australian cities?. Theoret. Appl. Climatol. 137, 441–457 (2019).
Beck, H. E. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci. Data 10, 724 (2023).
Lee, X. Eq. 2.29, in Fundamentals of Boundary-Layer Meteorology (Springer, 2023).
Pyrgou, A., Hadjinicolaou, P. & Santamouris, M. Urban-rural moisture contrast: Regulator of the urban heat island and heatwaves’ synergy over a mediterranean city. Environ. Res. 182, 109102 (2020).
Li, D. & Bou-Zeid, E. Synergistic interactions between urban heat islands and heat waves: The impact in cities is larger than the sum of its parts. J. Appl. Meteorol. Climatol. 52, 2051–2064 (2013).
Ngarambe, J., Nganyiyimana, J., Kim, I., Santamouris, M. & Yun, G. Y. Synergies between urban heat island and heat waves in Seoul: The role of wind speed and land use characteristics. PLoS ONE 15, e0243571 (2020).
Ma, X. & Wang, A. Evaluation and uncertainty analysis of the land surface hydrology in ls3mip models over China. Earth Space Sci. 11, e2023EA003391 (2024).
Ménard, C. B. et al. Meteorological and evaluation datasets for snow modelling at ten reference sites: description of in situ and bias-corrected reanalysis data. Earth Syst. Data Discuss. 2019, 1–34 (2019).
Ghorbany, S., Hu, M., Yao, S. & Wang, C. Towards a sustainable urban future: A comprehensive review of urban heat island research technologies and machine learning approaches. Sustainability 16, 4609 (2024).
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J.G. designed the study, performed the analysis and wrote the manuscript. K.Z. provided guidance on study design and contributed to manuscript revision. X.L. contributed to ideas and manuscript revision.
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Guo, J., Lee, X. & Zhang, K. Global spatiotemporal analysis of interactions between urban heat islands and extreme heat waves. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37372-7
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DOI: https://doi.org/10.1038/s41598-026-37372-7
