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Abstract Hailstorms cause billions of dollars in damage across the United States each year. Part of this cost could be reduced by increasing warning lead times. To contribute to this effort, we developed a nowcasting machine learning model that uses a 3D U-Net to produce gridded severe hail nowcasts for up to 40 min in advance. The three U-Net dimensions uniquely incorporate one temporal and two spatial dimensions. Our predictors consist of a combination of output from the National Severe Storms Laboratory Warn-on-Forecast System (WoFS) numerical weather prediction ensemble and remote sensing observations from Vaisala’s National Lightning Detection Network (NLDN). Ground truth for prediction was derived from the maximum expected size of hail calculated from the gridded NEXRAD WSR-88D radar (GridRad) dataset. Our U-Net was evaluated by comparing its test set performance against rigorous hail nowcasting baselines. These baselines included WoFS ensemble Hail and Cloud Growth Model (HAILCAST) and a logistic regression model trained on WoFS 2–5-km updraft helicity. The 3D U-Net outperformed both these baselines for all forecast period time steps. Its predictions yielded a neighborhood maximum critical success index (max CSI) of ∼0.48 and ∼0.30 at forecast minutes 20 and 40, respectively. These max CSIs exceeded the ensemble HAILCAST max CSIs by as much as ∼0.35. The NLDN observations were found to increase the U-Net performance by more than a factor of 4 at some time steps. This system has shown success when nowcasting hail during complex severe weather events, and if used in an operational environment, may prove valuable.