A High-Resolution Gridded Dataset for China's Monthly Sectoral Water Use

High-quality water use datasets are essential for advancing water resources research in changing environment. However, existing Chinese water use data, typically aggregated by administrative boundaries or watersheds, lack sufficient spatial and temporal resolution. This limitation hinders detailed analysis of human water use patterns and spatiotemporal variations. Here, we present the High-resolution Sectoral Water Use Dataset (HSWUD) for China mainland, covering the period from 1965 to 2022 and including sectors such as irrigation, manufacturing, thermal power cooling, and domestic water use. By integrating remote sensing land use data, population density maps, reanalysis meteorological data, geospatial information on thermal power plants and micro-level survey data from industrial enterprises, we developed a downscaling algorithm incorporating multiple covariates. This algorithm disaggregates provincial annual water use data to monthly grid cells at a 0.1° × 0.1° resolution, enhancing the spatial detail and seasonal distribution of water use across China. HSWUD shows a strong correlation with prefecture-level statistical data (R² = 0.88), while the captured spatiotemporal patterns are broadly consistent with existing datasets.