Year: 2025
Type: data-paper
Abstract: 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.
Source: Scientific Data
Authors: Qinqin Kong, Matthew Huber
Institution: Purdue University West Lafayette
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $2,490
Awards: G1270836760, G184917953, Innovations at the Nexus of Food, Energy, and Water Systems (INFEWS: U.S.-China): A multi-scale integrated modeling approach to managing the transition to sustainability, CyberTraining: CIU:Cross-disciplinary Training for Findable, Accessible, Interoperable, and Reusable (FAIR) science
OpenAlex ID: W4407401928