Near‐Real‐Time Monitoring of Global Terrestrial Water Storage Anomalies and Hydrological Droughts

Abstract Global terrestrial water storage anomaly (TWSA) products from the Gravity Recovery and Climate Experiment (GRACE) and its Follow‐On mission (GRACE/FO) have an approximately three‐month latency, significantly limiting their operational use in water management and drought monitoring. To address this challenge, we develop a Bayesian convolutional neural network (BCNN) to predict TWSA fields with uncertainty estimates during the latency period. The results demonstrate that BCNN provides near‐real‐time TWSA estimates that closely match GRACE/FO observations, with median correlation coefficients of 0.92–0.95, Nash‐Sutcliffe efficiencies of 0.81–0.89, and root mean squared errors of 1.79–2.26 cm for one‐ to three‐month ahead predictions. More importantly, the model advances global hydrological drought monitoring by enabling detection up to three months before GRACE/FO data availability, with median characterization mismatches below 16.4%. This breakthrough in early warning capability addresses a fundamental constraint in satellite‐based hydrological monitoring and offers water resource managers critical lead time to implement drought mitigation strategies.