The Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE-FO have provided unprecedented insights into terrestrial water storage (TWS) changes at regional to global scales. However, the coarse spatial resolution of GRACE data (~300–500 km) presents significant limitations for local-scale hydrological applications, such as groundwater monitoring, drought assessment, and basin-level water resource management. This review synthesizes the state-of-the-art downscaling techniques developed to enhance the spatial resolution of GRACE-derived data. These approaches can be categorized into statistical, machine learning (ML), and hybrid methods, highlighting their methodological foundations, predictive capabilities, and regional applicability. Case studies from the United States, India, China, Iran, and Sub-Saharan Africa demonstrate the effectiveness of downscaled GRACE data in identifying groundwater depletion hotspots, monitoring hydroclimatic extremes, and supporting transboundary water governance. Evaluation strategies commonly involve comparisons with in situ well observations, land surface models, and geodetic measurements such as GPS and InSAR. Despite notable progress, challenges remain related to data uncertainty, model transferability, lack of standardization, and computational requirements. Key research directions include the development of physics-informed deep learning models, real-time downscaling frameworks, and integration with upcoming satellite missions. This review underscores the transformative potential of downscaling GRACE data to meet the growing demands for high-resolution hydrological information in water-scarce and data-limited regions.