Learning Synthetic Extratropical Cyclone Models for Climate Extreme Risk Assessments Using Generative Models

Extratropical cyclones (ETCs) dominate mid-latitude wind hazards, yet their risk remains poorly quantified. Unlike tropical cyclones, ETCs lack scalable, physics-based downscaling methods because of their multiscale, asymmetric structure. As a result, probabilistic ETC risk assessment relies on computationally intensive numerical weather prediction models, limiting ensemble size and constraining estimates of extreme risk.

Here we introduce a data-driven generative downscaling framework that maps coarse-resolution reanalysis wind fields (ERA5, 25 km) to convection-permitting resolution (WRF, 4 km), resolving mesoscale structures essential for hazard and loss modeling. Across a broad range of ETC events, when applied to near-surface winds for flooding and energy application, the downscaled fields reproduce spatial organization, extremes, and kinetic-energy spectra consistent with high-resolution WRF simulations, while reducing computational cost by orders of magnitude. A key element of this success is to couple statistical inference with generative ML models, which ameliorates the data paucity issues for rare events. 

To extend beyond the historical record, we couple this downscaling model with a data-driven sampling and propagation model, which enables large ensembles of physically plausible high-resolution scenarios. This combined framework substantially improves estimation of tail risks, resolving well beyond the training data, that are inaccessible to observations and impractical to sample using conventional numerical models.