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Earth System Models (ESMs) are the state of the art for projecting theeffects of climate change. However, longstanding uncertainties in their abilityto simulate regional and local precipitation extremes and related processesinhibit decision making. Stakeholders would be best supported by probabilisticprojections of changes in extreme precipitation at relevant space-time scales.Here we propose an empirical Bayesian model that extends an existing skill andconsensus based weighting framework and test the hypothesis that nontrivial,physics-guided measures of ESM skill can help produce reliable probabilisticcharacterization of climate extremes. Specifically, the model leveragesknowledge of physical relationships between temperature, atmospheric moisturecapacity, and extreme precipitation intensity to iteratively weight and combineESMs and estimate probability distributions of return levels. Out-of-samplevalidation shows evidence that the Bayesian model is a sound method forderiving reliable probabilistic projections. Beyond precipitation extremes, theframework may be a basis for a generic, physics-guided approach to modelingprobability distributions of climate variables in general, extremes orotherwise.

Physics-guided probabilistic modeling of extreme precipitation under climate change