Abstract The Gravity Recovery and Climate Experiment (GRACE) satellite mission provides accurate observations of terrestrial water storage anomalies (TWSA), but their coarse spatial resolution (∼3°) limits sub‐regional‐scale applications. Existing downscaling methods rely on high‐resolution hydrometeorological inputs, which often underestimate the full magnitude of GRACE signals. Here, we develop a machine‐learning‐based iterative downscaling method that reproduces TWSA at 0.25° resolution while retaining nearly all of the original GRACE signals, using ERA5 soil moisture, precipitation, and temperature as inputs. We find that the downscaled TWSA has improved agreement with in situ groundwater levels compared to the original GRACE data, with higher correlation at over 63% of wells and reduced RMSE at more than 83% globally. The downscaled TWSA also retains an average correlation of 0.99 with original GRACE data at the basin scale, outperforming a previously released downscaling product. The downscaled TWSA data set is publicly available at https://doi.org/10.5281/zenodo.17265162.

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