Extreme Rainfall from Tropical Cyclones is Revealed by Kilometer-Scale Downscaling in Southeast Africa

Abstract Tropical cyclones in southeast Africa pose significant flood risks, driven by high winds that can cause storm surge flooding, and extreme rainfall often leading to devastating inland impacts. Understanding current and future flood risk from extreme rainfall in the region is hindered by the limited observational network. Although reanalysis and satellite rainfall data improve spatial coverage, their coarse resolution restricts the accurate representation of intense convective rainfall associated with tropical cyclones. To address this limitation, fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) reanalysis data were dynamically downscaled to a convection-permitting resolution using the Conformal Cubic Atmospheric Model (CCAM) from 2014 to 2023. This study assesses the ability of kilometer-scale CCAM simulations to improve the characterization of extreme rainfall from six impactful tropical cyclones affecting Malawi, Madagascar, and Mozambique. Comparisons are made against reanalysis data, satellite-derived rainfall, and rain gauge observations. Results show that CCAM captures higher rainfall totals and improves the underestimation bias of extreme hourly rainfall present in the other datasets. Additionally, CCAM reveals detailed rainband structures, including evidence of double rainbands within the eyewall, and inner and outer spiral rainbands. This work has demonstrated that the current reanalysis is not sufficient to properly characterize flood risk from tropical cyclones. The kilometer-scale model provides a more realistic simulation of flood-relevant rainfall. These advancements are essential for understanding and managing flood risk under current and future climate scenarios, supporting adaptive actions to mitigate tropical cyclone impacts. Significance Statement This study emphasizes the critical importance of high-resolution approaches for accurately assessing flood risks related to tropical cyclones in southeast Africa. The use of convection-permitting resolution significantly improves the representation of extreme convective rainfall. Results indicate that this approach captures extreme rainfall from tropical cyclones more accurately than existing reanalysis and satellite datasets, offering better insights into the complex rainband structures associated with cyclones. These advancements are important for informing effective flood risk management strategies and supporting adaptive measures in response to changing climate conditions. Such progress is essential for safeguarding vulnerable communities in Malawi, Madagascar, Mozambique, and beyond.