Updates to C-LSAT 2.1 and the development of high-resolution land surface air temperature and diurnal temperature range datasets

Abstract. High-resolution climate datasets are of critical importance for the comprehension of spatial and temporal variations in climate and hydrology. However, their development is significantly influenced by the availability, density, and quality of observational data. Building on the China global land surface air temperature 2.0 (C-LSAT 2.0) station data, we collected and integrated nearly 3000 additional station observations and conducted quality control and homogenization processing to complete the updates to the C-LSAT 2.1 dataset. The coverage of the C-LSAT 2.1 dataset has been significantly enhanced, further improving the representativeness of global land diurnal temperature range (DTR) data with greater spatial heterogeneity. Compared to C-LSAT 2.0, C-LSAT 2.1 shows consistent overall trends, except for a slight post-2010 increase for the Southern Hemisphere LSAT anomaly. Furthermore, we employed a “thin-plate spline (climatology) and adjusted inverse distance weighted (anomaly fields)” technical framework to develop a high-resolution (0.5°×0.5°) LSAT (C-LSAT HRv1) and DTR (C-LDTR HRv1) dataset covering January 1901–December 2023. Apart from discrepancies in 1901–1950 due to the limited number of observational stations, the C-LSAT HRv1 and C-LDTR HRv1 datasets effectively capture global and regional variation patterns for subsequent periods. The C-LSAT 2.1 dataset can be downloaded from https://doi.org/10.6084/m9.figshare.28255394.v1 (Wei et al., 2025a), while the C-LSAT HRv1 and C-LDTR HRv1 datasets are available at https://doi.org/10.6084/m9.figshare.28255505.v2 (Wei et al., 2025c) and https://doi.org/10.6084/m9.figshare.28255568.v2 (Wei et al., 2025b), respectively. They are also accessible via http://www.gwpu.net (last access: 11 July 2025).