  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.
