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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. Using the China global Land Surface Air Temperature 2.0 (C-LSAT 2.0) station data as a foundation, we collected and integrated nearly 3000 additional station observations and conducted the quality control and homogenization processing to complete the update of the C-LSAT 2.1 dataset. The coverage of Tavg, Tmax, and Tmin in the C-LSAT 2.1 dataset has been significantly enhanced, further enhancing 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 increase in LSAT anomaly observed in the Southern Hemisphere after 2010. Furthermore, we employ a 'Thin Plate Spline (climatology) + Adjust 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 from January 1901 to December 2023. Except for some differences existing during the period of 1901–1950 due to the limited number of observational stations, the C-LSAT HRv1 and C-LDTR HRv1 datasets effectively capture the corresponding variation patterns at both global and regional scales for the other 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.v1 (Wei et al., 2025c) and https://doi.org/10.6084/m9.figshare.28255568.v1 (Wei et al., 2025b), respectively. These can also be accessed at http://www.gwpu.net (last accessed: December 2024). [ABSTRACT FROM AUTHOR]