Magnitude of urban heat islands largely explained by climate and population


Urban heat islands (UHIs) exacerbate the risk of heat-related mortality associated with global climate change. The intensity of UHIs varies with population size and mean annual precipitation, but a unifying explanation for this variation is lacking, and there are no geographically targeted guidelines for heat mitigation. Here we analyse summertime differences between urban and rural surface temperatures (ΔTs) worldwide and find a nonlinear increase in ΔTs with precipitation that is controlled by water or energy limitations on evapotranspiration and that modulates the scaling of ΔTs with city size. We introduce a coarse-grained model that links population, background climate, and UHI intensity, and show that urban–rural differences in evapotranspiration and convection efficiency are the main determinants of warming. The direct implication of these nonlinearities is that mitigation strategies aimed at increasing green cover and albedo are more efficient in dry regions, whereas the challenge of cooling tropical cities will require innovative solutions.

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Fig. 1: Effect of background climate and population size on urban warming and its components.
Fig. 2: Urban warming and green spaces in Europe and South East Asia.
Fig. 3: Impact of background climate on the efficiency of heat mitigation strategies.

Data availability

The Global Urban Heat Island Data Set 2013 is available at (accessed on 7 December 2017). MERRA data were retrieved from (downloaded on 4 March 2018) while GPCC data are available at (accessed on 13 September 2016). MODIS albedo data are available at (accessed on 15 July 2018). Urban green cover data for EU and SEA cities are available, respectively, at,_towns_and_suburbs_-_green_cities#Further_Eurostat_information (accessed on 14 June 2017) and (accessed on 29 September 2017). A summary table containing the urban and climate characteristics of the cities analysed is also available on Code Ocean (

Code availability

The MATLAB code ( of the coarse-grained UHI model is available on Code Ocean (


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G.M. was supported by the The Branco Weiss Fellowship—Society in Science administered by ETH Zurich. E.B.-Z. acknowledges support by the US National Science Foundation under grant no. ICER 1664091, the SRN under cooperative agreement no. 1444758, and the Army Research Office under contract W911NF-15-1-0003 (program manager J. Barzyk). M.S. was supported by the Future Cities Laboratory at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and Singapore’s National Research Foundation (FI 370074016), under its Campus for Research Excellence and Technological Enterprise programme. We thank P. Edwards, J. Carmeliet, C. Küffer, and D. Richards for help and discussions at the beginning of this research.

Author information

G.M. designed the study, developed the model and conducted the analysis with contributions from S.F., G.G.K. and E.B.-Z. K.Y. and T.W.C. analysed albedo remote sensing observations. G.M. wrote the original draft of the manuscript with input from S.F., G.G.K. and E.B.-Z. M.S., K.Y., T.W.C., N.M. and P.B. reviewed and edited the manuscript. All authors discussed the results and contributed to the final version of the manuscript.

Correspondence to Gabriele Manoli.

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Peer review information Nature thanks Lahouari Bounoua, Ben Crawford and Qihao Weng for their contribution to the peer review of this work.

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Supplementary Methods, Supplementary Tables 1–6, Supplementary Figs 1–25 and Supplementary References.

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