Global Predictability of Marine Heatwave Induced Rapid Intensification of Tropical Cyclones.

Prediction of the rapid intensification (RI) of tropical cyclones (TCs) is crucial for improving disaster preparedness against storm hazards. These events can cause extensive damage to coastal areas if occurring close to landfall. Available models struggle to provide accurate RI estimates due to the complexity of underlying physical mechanisms. This study provides new insights into the prediction of a subset of rapidly intensifying TCs influenced by prolonged ocean warming events known as marine heatwaves (MHWs). MHWs could provide sufficient energy to supercharge TCs. Preconditioning by MHW led to RI of recent destructive TCs, Otis (2023), Doksuri (2023), and Ian (2022), with economic losses exceeding $150 billion. Here, we analyze the TC best track and sea surface temperature data from 1981 to 2023 to identify hotspot regions for compound events, where MHWs and RI of tropical cyclones occur concurrently or in succession. Building upon this, we propose an ensemble machine learning model for RI forecasting based on storm and MHW characteristics. This approach is particularly valuable as RI forecast errors are typically largest in favorable environments, such as those created by MHWs. Our study offers insight into predicting MHW TCs, which have been shown to be stronger TCs with potentially higher destructive power. Here, we show that using MHW predictors instead of the conventional method of using sea surface temperature reduces the false alarm rate by 30%. Overall, our findings contribute to coastal hazard risk awareness amidst unprecedented climate warming causing more frequent MHWs. Plain Language Summary: Tropical cyclones (TCs) that rapidly intensify pose a significant threat to coastal areas. Current prediction models struggle to accurately forecast these events due to the complex nature of underlying physical processes. This study focuses on improving predictions for a specific type of rapidly intensifying TCs influenced by prolonged ocean warming events known as marine heatwaves. By developing an ensemble machine learning model that considers storm and marine heatwaves characteristics, we provide a new tool for detecting rapid intensification events that are particularly challenging to forecast in favorable environments created by MHWs. These events tend to produce stronger TCs. The developed model demonstrated higher accuracy in detecting rapid intensification while reducing false alarms. This research enhances coastal hazard preparedness in the face of rising ocean temperatures. Key Points: Global hotspots for marine heatwave‐induced rapid intensifications identified, accounting for over $360 billion in losses only in 2021–2023An ensemble machine learning model is developed to predict this destructive subset of RI eventsWe quantify enhanced predictive skill achieved by using marine heatwave intensity metrics as predictors compared to sea surface temperature [ABSTRACT FROM AUTHOR]