Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields

Global Climate Models (GCMs) are essential tools for climate projections, but their coarse spatial resolution (~100–200 km) and systematic biases limit their direct use for regional impact studies. This limitation is particularly critical for wind-related applications, such as wind energy assessments, which require spatially coherent, multivariate, and physically plausible near-surface wind fields. Classical statistical downscaling and bias correction methods partly address this issue, yet they often fail to preserve spatial structure, inter-variable consistency, and robustness under climate change, especially when applied to high-dimensional climate fields.

Recent advances in generative machine learning offer new opportunities for downscaling and bias correction without relying on explicitly paired low- and high-resolution datasets. Such methods can generate fine-scale, physically consistent fields conditioned on large-scale climate patterns. However, many existing approaches remain difficult to interpret and challenging to deploy in operational climate impact studies.

In this work, we apply SerpentFlow, an interpretable, generative, domain alignment framework, to the multivariate downscaling and bias correction of wind variables from the ACCESS Earth System Model over the French territory at a resolution of approximately 25 km, under the SSP2-4.5 scenario. The framework constructs pseudo low-/high-resolution pairs by explicitly separating large-scale spatial patterns from small-scale variability, aligning large-scale components between model outputs and observations, and learning conditional fine-scale variability via a flow-matching generative model. This approach enables the generation of realistic fine-scale wind fields while preserving physical plausibility and inter-variable correlations.

We evaluate the method on multiple near-surface wind variables, including wind speed, zonal and meridional components, and maximum wind speed, and compare its performance to widely used statistical downscaling and multivariate bias correction methods, such as CDF-t and R2D2. Evaluation metrics include the preservation of spatial structure, inter-variable correlation, extremes, and robustness under future climate conditions. We find that SerpentFlow significantly improves spatial coherence and consistency among wind components compared to baseline methods, while maintaining realistic distributions and extreme events. Ensemble simulations further illustrate the method’s ability to capture stochastic fine-scale variability, an important aspect for climate risk assessment and energy resource studies.

Our results demonstrate that interpretable generative domain adaptation methods can address critical limitations of classical downscaling techniques, providing high-resolution, physically consistent, and multivariate-consistent wind fields suitable for climate impact and energy applications. This work highlights the potential of SerpentFlow as a flexible tool for operational downscaling tasks, capable of adapting to different GCMs, resolutions, and scenarios without requiring paired training data. The framework thus represents a promising avenue for generating reliable, high-resolution climate information to support regional adaptation and wind energy planning.