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Hailstorms cause heavy losses, especially when their hailstones reach a large size. One of the European regions most affected by these severe atmospheric events is southern France, where a valuable and extensive hailpad network has been operational for more than three decades. These direct observations are extremely useful because they allow for the definitive verification of hailfall at the ground. Space-based sensors have seen increasing importance in hail monitoring. Global Precipitation Measurement (GPM) is an international mission designed to advance precipitation measurements from multispectral sensors. The GPM core satellite carries a powerful and unprecedented Dual-Frequency Precipitation Radar (DPR) for studying 3D precipitation characteristics. The objective of the present study is to evaluate the DPR sensor ability to identify hailstorms. We identified eight hailstorms over France where DPR data were coincident with ground-based observations from a hailpad network during 2014–2021. In addition, variables provided by the DPR sensor indicative of hail presence were studied and five detection algorithms were tested. This research serves as background for future work and the development of prediction algorithms based on empirical relationships with GPM data. • 2387 hailfall records between 2014 and 2021 were found. • Eight hailstorms were detected by DPR scans. • Hail forecast variables were extracted from the DPR sensor and analyzed. • Five hail-detection algorithms were tested on the storms. [ABSTRACT FROM AUTHOR]