Tropical Cyclone Rainfall Spatial Asymmetries in the Lesser Antilles

This study utilises GPM‐IMERG satellite rainfall estimates to analyse the asymmetric rainfall patterns of 35 tropical cyclones (TCs) that passed over the Caribbean Lesser Antilles (CLA) islands between 2000 and 2020. The aim is to evaluate whether TC rainfall spatial–temporal patterns vary systematically across Saffir‐Simpson (SS) categories to strengthen early warning protocols. The widely used SS scale focuses on wind intensity, overlooking the significant role of TC rainfall in flooding. In the CLA, landfalling and offshore TCs cause significant rainfall impacts across multiple islands and storms exhibit high variability in trajectories, durations and SS category transitions. The study examines the relationship between SS categories and TC rainfall characteristics, using maximum sustained winds (MSW) to classify storms into categories. Linear and monotonic correlation analyses are performed using continuous MSW values to explore overall associations with rainfall; however, reporting and interpretation focus on discrete SS categories, which represent the operationally relevant storm intensity classes for hazard analysis. Although SS categories represent linearly increasing storm intensity, rainfall exhibits nonlinear patterns with significant variability within and across categories. Contrary to common expectations, TCs in the highest SS categories (H4 and H5) do not consistently produce the highest rainfall. Tropical storms can generate rainfall amounts comparable to or exceeding those of hurricanes and hurricanes in categories H1–H3 sometimes produce more intense rainfall than those in H4 and H5. The study also examines rainfall asymmetry in the four quadrants (NE, SE, SW, NW) around the TC eye by SS category. Rainfall is generally concentrated in the NE and SE quadrants, but the SW and NW quadrants also contribute substantially, reflecting the spiralling nature of TCs, which continuously refuel rainfall in all quadrants throughout the storm's lifecycle. These findings underscore the complexity of TC rainfall patterns and the need to integrate such variability into early warning systems to enhance disaster preparedness and mitigation strategies.