Investigating the Robustness of Extreme Precipitation Super-Resolution Across Climates
This work addresses the challenge of projecting extreme precipitation for climate impact assessments, offering a model-agnostic diagnostic for robustness under climate change, though it is incremental as it builds on existing downscaling methods.
The study tackled the problem of downscaling extreme precipitation from coarse climate models by super-resolving distribution parameters directly, demonstrating that vector generalized linear and additive models can achieve this in a perfect-model framework over Switzerland, with results including the identification of an upper limit on the super-resolution factor based on spatial correlations.
The coarse spatial resolution of gridded climate models, such as general circulation models, limits their direct use in projecting socially relevant variables like extreme precipitation. Most downscaling methods estimate the conditional distributions of extremes by generating large ensembles, complicating the assessment of robustness under distributional shifts, such as those induced by climate change. To better understand and potentially improve robustness, we propose super-resolving the parameters of the target variable's probability distribution directly using analytically tractable mappings. Within a perfect-model framework over Switzerland, we demonstrate that vector generalized linear and additive models can super-resolve the generalized extreme value distribution of summer hourly precipitation extremes from coarse precipitation fields and topography. We introduce the notion of a "robustness gap", defined as the difference in predictive error between present-trained and future-trained models, and use it to diagnose how model structure affects the generalization of each quantile to a pseudo-global warming scenario. By evaluating multiple model configurations, we also identify an upper limit on the super-resolution factor based on the spatial auto- and cross-correlation of precipitation and elevation, beyond which coarse precipitation loses predictive value. Our framework is broadly applicable to variables governed by parametric distributions and offers a model-agnostic diagnostic for understanding when and why empirical downscaling generalizes to climate change and extremes.