A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)

Abstract

Abstract. Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax⁡ and Tmin⁡) is particularly valuable and critically needed in the scientific and policy communities but is still not available. In this paper, we developed a global dataset of daily Tmax⁡ and Tmin⁡ at 1 km resolution over land across 50∘ S–79∘ N from 2003 to 2020 through the combined use of ground-station-based Ta measurements and satellite observations (i.e., digital elevation model and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors in our estimates ranged from 1.20 to 2.44 ∘C for Tmax⁡ and 1.69 to 2.39 ∘C for Tmin⁡. We found that the accuracies were affected primarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents a negative relationship between Ta and elevation and a positive relationship between Ta and land surface temperature; it captured spatial and temporal patterns of Ta realistically. This global 1 km gridded daily Tmax⁡ and Tmin⁡ dataset is the first of its kind, and we expect it to be of great value to global studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data have been published by Iowa State University at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022).