SPIN (v1.0): A spontaneous synthetic tropical cyclone model empowered by NeuralGCM for hazard assessment

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Abstract
A hybrid framework for simulating SPontaneous synthetic tropical cyclones (TCs) with realistic INtensity, hereafter SPIN, is developed for TC risk assessment. Unlike earlier statistical synthetic-TC models, SPIN retains intra-month variability and provides the ambient environment for each storm. The SPIN model leverages a Neural General Circulation Model (NeuralGCM) to simulate spontaneously generated TC tracks, and then couples a dynamical TC intensity model to estimate their intensity evolution based on the large-scale environment. It reproduces key features of observed TC climatology, including interannual variability, seasonal cycle, genesis and track patterns, lifetime-maximum intensity, regional landfall-intensity return periods and inter-genesis intervals. It also improves the representation of interannual TC variability in basins where ENSO-related influences are spatially mixed. Beyond individual TC events, the model demonstrates improved skill in representing multiple tropical cyclone events (MTCEs), capturing their year-to-year variability and cluster size. SPIN advances TC risk assessment by enabling compound TC hazards, such as MTCEs and beyond, to be assessed as dynamically organized events rather than statistical co-occurrences alone.
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