Next-Generation Climate Projections: Insights from Blending Bias Correction with Super Resolution over Complex Terrain

Regional Climate Models (RCMs) are vital for capturing mesoscale variability, however remain too coarse for impact assessments in complex topographies like Switzerland. In this study, we bridge the "km-scale gap" by introducing a generative super resolution pipeline to downscale EURO-CORDEX ensemble to a 1 km grid over Switzerland.

We establish the added value of a deterministic residual U-Net, pixel-based as well as generative residual Latent Diffusion over operational baselines and conventional bias correction (BC) methods such as Cumulative Distribution Function - transform (CDF-t), Empirical Quantile Mapping (EQM) and dynamical Optimal Transport Correction (dOTC). Our results demonstrate that super resolved fields have superior distributional skill, better visual fidelity of fields, shows improved  trend preservation and representation of interannual variability across diverse biogeographical regions  and major population centres such as Bern, Zurich and Locarno. Further, as demonstrated by a marked reduction in bias for  20-, 50-, and 100-year return levels of multi-day precipitation totals, super resolution (SR) also complements BC for improved representation of extremes in our km-scale downscaled EUROCORDEX. Our findings establish that while BC methods remain essential for distributional fidelity, residual generative models offer a potent, actionable pathway for producing high-resolution climate information from coarse climate fields.