Physics-guided probabilistic modeling of extreme precipitation under climate change

TL;DR

This paper introduces a physics-guided Bayesian model that improves probabilistic projections of extreme precipitation under climate change by leveraging physical relationships and ESM skill assessments.

Contribution

It presents a novel empirical Bayesian framework that incorporates physical knowledge to enhance the reliability of climate extreme projections.

Findings

Out-of-sample validation confirms the model's reliability.

Physics-guided weighting improves extreme precipitation estimates.

Framework applicable to other climate variables.

Abstract

Earth System Models (ESMs) are the state of the art for projecting the effects of climate change. However, longstanding uncertainties in their ability to simulate regional and local precipitation extremes and related processes inhibit decision making. Stakeholders would be best supported by probabilistic projections of changes in extreme precipitation at relevant space-time scales. Here we propose an empirical Bayesian model that extends an existing skill and consensus based weighting framework and test the hypothesis that nontrivial, physics-guided measures of ESM skill can help produce reliable probabilistic characterization of climate extremes. Specifically, the model leverages knowledge of physical relationships between temperature, atmospheric moisture capacity, and extreme precipitation intensity to iteratively weight and combine ESMs and estimate probability distributions of return levels. Out-of-sample validation shows evidence that the Bayesian model is a sound method for deriving reliable probabilistic projections. Beyond precipitation extremes, the framework may be a basis for a generic, physics-guided approach to modeling probability distributions of climate variables in general, extremes or otherwise.