EMPIRIC_TC: A Site‐Specific AI‐Based Emulator for Tropical Cyclone Hazards Using Fourier Neural Operators

Abstract Tropical Cyclones (TCs) pose significant and evolving threats to Pacific Island Countries and Territories (PICTs) healthcare systems. Changes in cyclone intensity, frequency, and spatial distribution due to anthropogenic climate change are likely to increase these threats. Accessible information on future uncertainty of high intensity TCs can enable adaptation that reduces the severity of systemic disruptions. However, the infrequency of high‐intensity TC events in historical and GCM data and the small and dispersed geometries of PICTs make modeling the statistics of TC impacts challenging. In this paper, we try to close this gap by developing EMPIRIC_TC, a deep‐learning emulator of a statistical‐dynamical TC model, to provide local, accessible, ensemble projections of high‐intensity TCs in the South Pacific. We implement EMPIRIC_TC using a Fourier Neural Operator and show that it outperforms a UNet and Nearest Neighbors regression model at predicting facility relevant TC hazard and reproducing ensemble means. We then explore fundamental limits to the predictability of infrequent extreme events from climatic conditions and show their affect on deep learning model performance. The emulator allows for high‐skill, adaptable, and near‐instantaneous projections of TC hazards, estimation of climate model and internal variability‐related uncertainty, and evaluating projection uncertainty. The emulation system, EMPIRIC_TC, is available through a web portal for the use of PICT collaborators.