Urban land use and anthropogenic heat (AH) emission can considerably influence the human thermal comfort during extreme heat events. In this study, a spatially heterogeneous AH emission data and updated urban land use data are integrated into the Weather Research and Forecasting model to simulate the physical processes of urban warming during summer. Simulations conducted in the Yangtze River Delta (YRD) of east China suggest that the mean urban heat island intensity reaches 1.49 °C in urbanized areas during summer, with AH emission making a considerable contribution. The warming effect due to urban land use is intensified during extremely hot days, but in contrast, the AH effects are slightly reduced. Urban development increases the total thermal discomfort hours by 27% in the urban areas of YRD, with AH and urban land use contributing nearly equal amount. By limiting the daytime latent heat release, urban land use reduces the daily maximum heat stress particularly during extremely hot days; however, such alleviations can be offset by the AH emission. Strategies for mitigation of urban heat island effect and heat stress in cities should therefore include measures to reduce AH emission.

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A high temporal–spatial resolution (1 hr/1 km) gridded temperature data set with a long time‐series (2006–2015) is established for Zhejiang province, China, which has a complex terrain, including plains, hills, basins, mountains, islands and oceans. A comprehensive analysis method is used to combine regional surface station data with reanalysis data from the European Centre for Medium‐Range Weather Forecasts (ECMWF) such that the observations at the stations are reproduced, and the reanalysis data provide the spatial structure for the interpolation. The elevation correction method and inverse distance‐weighted interpolation method are selected for comparison. The quality of the data set is evaluated and analysed by comparison with the cross‐validation results, third‐party data validation and other gridded data sets. The results show that the comprehensive analysis method provides the best outcome, followed by the inverse distance‐weighted method, and the reanalysis data elevation correction method has the worst result. The temperature error obtained from the gridding methods increases with altitude, decreases with increasing station density and varies with different synoptic conditions. The obtained data set can describe the temperature feature of peaks and valleys more precisely, which produces lower errors and higher spatial correlation co‐efficients compared with observation than the other gridded data sets. Therefore, the established data set can meet the needs of fine meteorological businesses and has potential applications in many fields.