Fast emulation of climate models for precipitation and flood impact modelling using autoregressive video diffusion

The accurate assessment of extreme flood events and their associated losses requires massive sample sizes (e.g., 50,000+ years of weather data) for statistical robustness and a comprehensive coverage of event characteristics. Generating such a large dataset using dynamical Earth System Models would be extremely computationally intensive, so instead, we propose a lightweight and computationally efficient climate emulator built upon a video diffusion architecture. 

The model is trained to reproduce the statistical properties and physical dynamics of the Community Earth System Model version 2 (CESM2) over Europe. It operates autoregressively to generate synthetic, multivariate, daily atmospheric data (including temperature, specific humidity, wind vectors, and surface pressure) at ~100 km resolution. The model utilizes a U-Net architecture that is conditioned on previous time-steps to produce and evolve weather patterns with spatial and temporal consistency. To enhance the stability of long-term generation and improve the faithful reproduction of extremes, we employ a seasonality-aware standardization scheme, training the model to learn the dynamics in anomaly space rather than physical space.

We demonstrate that this approach successfully reproduces the complex spatiotemporal dependencies within CESM2, captures atmospheric dynamics, including the frequency and persistence of dominant circulation types, and can maintain stability over multi-decadal generation windows. Furthermore, the output of this emulator can be fed into existing downscaling models to produce higher resolution multivariate meteorological data fields to drive downstream impact models. We validate this full modeling chain by demonstrating that the resulting hydrological statistics exhibit physical characteristics consistent with the CESM2-driven benchmark.

This computationally efficient generative model offers a pathway to generating thousands of years of physically consistent flood events.