Assessing Stochastic Interpolants for Downscaling of Climate Extremes

Assessing Stochastic Interpolants for Downscaling of Climate Extremes

 

Erik Larsson, Ramón Fuentes-Franco, Mikhail Ivanov and Fredrik Lindsten

 

Assessing climate-related extremes at regional scales requires high-resolution information, typically obtained from dynamical regional climate models (RCMs). However, the computational expense of RCMs limits ensemble size and restricts the exploration of uncertainty. To address this challenge, we introduce a probabilistic machine-learning downscaling framework based on stochastic interpolants, trained to emulate 12 km HCLIM fields from coarse Earth System Model (ESM) output. By leveraging the stochastic interpolant framework, we construct a generative model that learns a direct mapping from coarse ESM inputs to high-resolution RCM simulations. This contrasts with standard diffusion-based approaches, where the model learns to transform Gaussian noise into RCM  states. Our preliminary results indicate that the stochastic interpolant formulation provides a more effective and stable learning objective for the downscaling task.

 

A comprehensive evaluation across Europe for 1985–2014 shows that the emulator accurately reproduces the climatological distribution and magnitude of daily precipitation extremes. Maximum daily precipitation fields capture orographic and coastal hotspots seen in HCLIM, such as the Alps, western Norway, the Dinaric Alps, and the western Iberian Peninsula.

 

For precipitation exceeding the local 95th percentile, the emulator achieves a domain-mean Matthews Correlation Coefficient (MCC) of 0.35. It maintains stronger spatiotemporal synchronisation with the ESM than the RCM itself, with an MCC of 0.46 against EC-Earth3-Veg compared to 0.35 for HCLIM. This indicates that the emulator follows the large-scale dynamics imposed by the driving ESM, while reproducing the fine-scale intensity and spatial structure of extremes characteristic of the RCM.

 

For temperature extremes, skill is even higher, with MCC values exceeding 0.7 across most of Europe, confirming robust reproduction of warm-event timing and spatial extent. The emulator also correctly represents daily temperature–precipitation covariability, including the transition from positive correlations in winter to negative correlations in summer, and reproduces the geographical pattern of compound hot-dry events, although with regional biases consistent with the driving model.

 

Overall, these results show that the stochastic interpolant downscaling framework provides a computationally efficient pathway to generate large, high-resolution ensembles that retain ESM dynamics while delivering RCM-like representations of climate extremes, offering new opportunities for climate-risk assessment, attribution studies, and impact modelling.