A 1-km daily high-accuracy meteorological dataset of air temperature, atmospheric pressure, relative humidity, and sunshine duration across China (1961–2021)

Abstract. Fine-resolution and high-accuracy meteorological datasets are essential for understanding climate change processes and their cascading impacts on hydrology, water resources management, and ecological systems. In this study, we present a nationwide, high-resolution dataset of six daily meteorological variables across China from 1961 to 2021, including average temperature, maximum temperature, minimum temperature, atmospheric pressure, relative humidity, and sunshine duration. The dataset was generated through a hierarchical reconstruction framework that utilizes daily observations from 2345 meteorological stations across China, combined with station-level topographic attributes (latitude, longitude, and elevation). By decoding the nonlinear relationships among six meteorological variables and their spatial covariates, the framework enables the generation of gridded daily fields at 1 km resolution with spatial continuity and internal consistency. Validation against 118 in-situ stations confirms that the dataset achieves high accuracy across all variables, with average, maximum, and minimum temperatures exhibiting minimal errors (median RMSEs: 1.03 °C, 1.19 °C, 1.34 °C; median MEs: -0.09 °C, -0.10 °C, -0.08 °C) and high consistency with in-situ data (median CCs: 1.00, 0.99, 0.99). Atmospheric pressure shows minimal error (median RMSE: 2.48 hPa; median ME: -0.02 hPa) and high consistency (median CC: 0.98). Although relative humidity has slightly weaker accuracy (median RMSE: 6.02 %; median ME: -0.5 %; median CC: 0.90), it still surpasses standard benchmarks. Sunshine duration maintains high precision (median RMSE: 1.48 h; median ME: 0.05 h; median CC: 0.93), demonstrating overall excellent product quality. Further comparison reveals that in high-altitude and topographically complex regions, the reconstructed product demonstrates higher actual accuracy than suggested by station-to-grid validation, as spatial mismatches between stations and grid cells lead to systematic underestimation. Free access to the dataset available at https://doi.org/10.11888/Atmos.tpdc.301341 or https://cstr.cn/18406.11.Atmos.tpdc.301341.