A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics

Increasing heat stress with climate change will threaten human health and cause broad social and economic impacts. The evaluation of such impacts depends on a reliable dataset of heat stress projection. Here we present a global dataset of the future projection of dry-bulb, wet-bulb and wet-bulb globe temperature under 1-4°C of global warming levels compared with the preindustrial era using output from 16 CMIP6 global climate models (GCMs). The dataset was bias-corrected against ERA5 reanalysis by adding the GCM-simulated climate change signal onto ERA5 baseline (1950-1976) at 3-hourly frequency. The resulting datasets are provided at fine spatial (0.25° × 0.25°) and temporal (3-hourly) resolution. We validate the bias-correction approach and demonstrate that it substantially improves the GCMs' ability to reproduce both the annual average and entire range of quantiles for all metrics within an ERA5 reference climate state. We expect the dataset to benefit future work on estimating projected changes in both mean and extreme heat stress and assessing consequential health and social-economic impacts.