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

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 & LCIRC; S-79 & LCIRC; 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 & LCIRC;C for Tmax and 1.69 to 2.39 & LCIRC;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 (Zhang and Zhou, 2022).

Automated summary

The paper presents the development of a global gridded dataset of daily maximum and minimum air temperature at a resolution of 1 km. The dataset accurately captures the relationships between temperature and elevation as well as land surface temperature, and provides valuable information for global studies such as urban heat island phenomenon, hydrological modeling, and epidemic forecasting.