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

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

[学科分类] 眼科学