Projecting compound flood hazards induced by tropical cyclones in Southeast China using MRI-AGCM3-2-S climate model

Many low-lying coastal areas are highly vulnerable to compound flooding induced by tropical cyclones (TCs), which often generate storm tides, intense rainfall, and elevated river discharge simultaneously. Despite their significant impacts, the spatiotemporal changes and future hazards of TC-induced compound flooding under climate change remain poorly understood for many coastal regions. This study presents a novel integrated hazard assessment framework to evaluate TC-induced compound flooding across 60 cities in Southeast China, one of the world’s most TC-prone regions. Using TC tracks detected from the MRI-AGCM3-2-S climate model combined with simulations using a cascade of hydrodynamic models, we simulate undefended compound flood hazards driven by rainfall, storm tides, and river discharge under the current climate (1960–2014) and future projections under the SSP585 scenario (2015–2099). Our results indicate that, according to simulations using the MRI climate model, more than half of the 60 cities in Southeast China are projected to experience more severe TC-induced compound flooding under future climate scenarios. Shanghai, in particular, is projected to experience the largest increases in compound flood hazard, with the maximum flood volume rising by 83.6%. Our analysis of flood drivers shows that flooding in 6 cities transition from single driver to compound-dominated type. For instance, Suzhou and Nantong are projected to shift from rainfall-dominated flooding to compound-dominated flooding. A detailed case study of Shanghai indicates a significant spatial expansion of areas dominated by compound flood drivers, with the flood-prone area fraction projected to increase from 60.6% to 78.1% under future climate scenario. These findings highlight the growing hazards of TC-induced compound flooding in Southeast China due to climate change. We recommend future research to increase the robustness of the approach by including multiple climate models and collect data on flood defenses to further refine the model outcomes.