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We present a novel Bayesian statistical approach to computing model\udweights in climate change projection ensembles in order to create\udprobabilistic projections. The weight of each climate model is obtained by\udweighting the current day observed data under the posterior distribution\udadmitted under competing climate models. We use a linear model to describe\udthe model output and observations. The approach accounts for uncertainty in\udmodel bias, trend and internal variability, including error in the\udobservations used. Our framework is general, requires very little\udproblem-specific input, and works well with default priors. We carry out\udcross-validation checks that confirm that the method produces the correct\udcoverage

A Bayesian posterior predictive framework for weighting ensemble regional climate models