Generalizable Generative Downscaling: Maintaining Physical Consistency from Reanalysis to GCMs and Hydrological Applications

Bridging the scale gap between coarse General Circulation Models (GCMs) and high-resolution data, e.g. the type required for hydrological assessment, remains a significant challenge. While dynamic downscaling via Regional Climate Models (RCMs) offers guarantees of physical consistency, its computational cost prohibits creating the large-volume ensembles required for catastrophe risk assessment. This work presents a matured Generative Diffusion Model (DM) framework that achieves high-resolution (10 km) downscaling across Europe with significantly lower computational overhead than similar methods. Crucially, we demonstrate zero-shot transferability by downscaling the 100-member CESM2 Large Ensemble (CESM2-LENS), despite the model being trained exclusively on reanalysis data.

To move beyond traditional pixel-wise metrics, we employ a multi-scale validation strategy: (1) Distributional integrity, recovering extreme precipitation tails; (2) Spatial consistency, using Radially Averaged Log Spectral Density to confirm correct energy distribution from convective scales to synoptic systems; and (3) Temporal coherence, ensuring the chronological sequences required for realistic soil moisture evolution. Finally, we provide an "end-to-end" validation by forcing the Wflow distributed hydrological model. The resulting discharge simulations capture historical extremes across diverse European catchments, proving that the generative output is not merely visually plausible but physically functional. This framework offers a scalable, computationally efficient pathway for generating the massive synthetic event sets required for risk assessment in a non-stationary climate.