A physically guided deep learning reconstruction of terrestrial water storage anomalies at 0.1° across China

Abstract. Terrestrial water storage (TWS), comprising all surface and subsurface water components, is a key indicator of water availability. The Gravity Recovery and Climate Experiment (GRACE) satellite mission provides large-scale estimates of TWS anomalies (TWSA), but its coarse spatial resolution (3°, approximately 300 km) limits the analysis of hydrologic processes at sub-regional scales. Using a physically-guided deep learning framework, we downscale TWSA from the original 3° GRACE mascons to 0.1° (approximately 10 km) across China, generating a standard version (2002–2019) with comprehensive observations used for model constraints and independent evaluation and an extended version (2020–2023) to support more recent hydrologic analyses. The downscaled TWSA preserves large-scale GRACE signals at the 3° grid scale (median correlation coefficient (CC): 0.95; root-mean-square error (RMSE): 1.38 cm) and basin scale (median CC: 0.94; RMSE: 1.72 cm), with a low median uncertainty (0.88 cm) across China. Its reliability is supported by high consistency with physically informed TWSA spatial patterns at the 0.1° resolution (median CC: 0.91) and internally consistent water balance closure beyond the native GRACE resolution (median CC: 0.80; RMSE: 1.44 cm). Evaluation against independent observations demonstrates that the downscaled TWSA agrees well with groundwater variations in intensively irrigated regions (CC: 0.65 for irrigation intensity > 50 %) and annual glacier elevation change in cryospheric areas (CC: 0.97). The datasets improve fine-scale characterization of TWS variability and associated hydrologic processes in China, and can be used as a reference for evaluating performance of high-resolution hydrologic models. The two versions of the dataset are available at https://doi.org/10.5281/zenodo.19502906.