These data contain estimates of temperature-related human mortality, as well as the associated economic assessments, related to urban heat islands for 85 European cities over the years 2015-2017. They are based on temperature-mortality relationships from Masselot et al. 2023 and 100m resolution UrbClim urban climate model simulations of near-surface air temperature (De Ridder et al. 2015, Hooyberghs et al. 2019), re-gridded to 500m resolution. Details of the methodology are provided in the associated paper: Huang, W.T.K. et al. Economic valuation of temperature-related mortality attributed to urban heat islands in European cities. Nat Commun 14, 7438 (2023). https://doi.org/10.1038/s41467-023-43135-z And associated core analysis code is available on GitHub at https://github.com/hkatty/Paper_UHI_mortality_Europe (doi:10.5281/zenodo.8429209). The content of the files are as follows: spatial_timeseries zip files: These contain the most unprocessed attributable fraction estimates, with the exposure-response relationships applied to the modelled temperature, prior to any further processing. uhi csv files: These are tables of the average mortality and years of life lost, as well as associated economic assessment, related to urban heat islands for each city. They are identical to Tables S4-S11 in the supplementary materials of the above paper. spatial_maps_time_averaged_diff_from_rural.zip: Spatial maps showing the difference from the rural average for each day and grid box, then averaged over time. data_urbanruralavg_timeseries.nc: Time series of urban and rural averages, as well as the difference between the two (i.e. the urban heat island effect). avg_diff_from_rural_urbanrural.nc: The above timeseries file temporally aggregated. simulated_urbanruraldiff_timeseries.zip: Time series of urban-rural difference in attributable fraction for 1000-member ensembles representing uncertainties in the exposure-response relationships as captured by Monte Carlo simulations. simulated_urbanruraldiff_averaged.zip: The above simulated timeseries temporally aggregated. Some variables explained: fAF = forward attributable fraction (i.e. fraction of total mortality associated with a single day's temperature, cumulative over lag time) fAD = forward attributable deaths (i.e. equivalent to fAF but for number of deaths) tas = temperature heat_ex = average over heat extreme days (i.e. the warmest 2% days in 2015-2017 for the city) cold_ex = average over cold extreme days (i.e. as heat_ex but for the coldest 2% days) heat = average over days warmer than the age-dependent optimal temperature cold = average over days colder than the age-dependent optimal temperature heat_count = number of days warmer than the optimal for the age group, note that for combined 2085.1 and 2085.5 age groups, days are counted if it is considered warm for at least one age group (therefore heat_count + cold_count ≠ total days over period) cold_count = number of days colder than the optimal for the age group rural = rural average imd = land imperviousness popden = population density age groups: 20 = 20 to 4445 = 45 to 6465 = 65 to 7475 = 75 to 8485 = 85 and over2085.1 = all above age groups combined, weighted by the local population age structure2085.5 = all above age groups combined, weighted by the 2013 European standard population age structure References: De Ridder, K., Lauwaet, D., and Maiheu, B., (2015): UrbClim – A fast urban boundary layer climate model. Urban Climate, 12, 21–48. https://doi.org/10.1016/J.UCLIM.2015.01.001. Hooyberghs, H., Berckmans, J., Lauwaet, D., Lefebre, F., and De Ridder, K., (2019): Climate variables for cities in Europe from 2008 to 2017. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.c6459d3a. Masselot et al. (2023): Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe, The Lancet Planetary Health, https://doi.org/10.1016/S2542-5196(23)00023-2.