Extreme rainfall from tropical cyclones is revealed by km-scale downscaling in Southeast Africa

Abstract

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, ERA5 reanalysis data was dynamically downscaled to a convection-permitting resolution using the Conformal Cubic Atmospheric Model (CCAM) from 2014 to 2023. This study assesses the ability of km-scale CCAM simulations to improve the characterisation 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 characterise flood risk from tropical cyclones. The km-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.