HiMIC-Daily: A high-resolution (daily and 1 km) multi-indicator atmospheric moisture collection over China, 2003–2020

Abstract. Near-surface atmospheric moisture is a fundamental component of the hydrological cycle and plays a key role in regulating land-atmosphere exchanges and surface energy partitioning. Reliable daily high-resolution moisture data are essential for regional climate analysis and fine-scale applications, particularly for capturing short-term variability and extreme moisture dynamics. With complex terrain and a dense population, China is highly vulnerable to extreme hydro-meteorological extremes, yet existing moisture products over China are largely constrained by coarse temporal resolution, insufficient spatial detail, and limited indicators. Here, we present HiMIC-Daily, a seamless daily 1-km-resolution near-surface atmospheric moisture dataset for China, 2003–2020. HiMIC-Daily provides a comprehensive suite of six widely used indicators that characterize atmospheric moisture from different perspectives: actual vapor pressure (AVP), dew point temperature (DPT), mixing ratio (MR), relative humidity (RH), specific humidity (SH), and vapor pressure deficit (VPD). This dataset is generated using the Light Gradient Boosting Machine (LightGBM) framework, which integrates in-situ observations from 2419 meteorological stations with multiple environmental and temporal covariates, including ERA5-Land derived near-surface temperature and DPT, AVP, land surface temperature, topography, and day of year. Validation against observations shows that HiMIC-Daily achieves robust performance across all six indicators, with R² values ranging from 0.877 to 0.989. The strongest performance is obtained for AVP, DPT, MR, and SH, with R² values exceeding 0.985, and error metrics remain within acceptable ranges for all indicators (e.g., mean absolute error of 0.677 hPa and a root mean square error of 0.933 hPa for AVP). Compared with two existing coarse resolution products, HiMIC-Daily provides finer spatial detail, higher accuracy, and more realistic temporal variability across different climatic regions. These capabilities support spatially explicit studies of climate variability and environmental processes. The HiMIC-Daily dataset is publicly available at https://doi.org/10.11888/Atmos.tpdc.303449.