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In a previous study, we developed a Bayesian methodology for combining multi-model climate change simulations into a single probabilistic projection which addresses changes in inter- annual variability beyond changes in mean temperature, and which explicitly considers time-depen- dent model biases. We tested 2 different but equally plausible bias assumptions referred to as 'con- stant bias' and 'constant relationship'. The former assumes that the biases in control and scenario periods are approximately constant, following the implicit assumption in most climate change stud- ies. The latter approach follows seasonal forecasting procedures by assuming an approximate linear relationship between observed and simulated seasonal temperatures. In the present study we gener- alized this approach by combining the 2 bias assumptions into a single probabilistic projection. In cases where the 2 assumptions yield conflicting results, our methodology implicates a broader prob- ability density function, thereby reflecting the increased level of uncertainty. We applied the new method to area-mean seasonal temperature distributions from global/regional climate model simula- tions of the ENSEMBLES project. Results are presented for changes in mean and variability between control (1961-1990) and scenario (2021-2050) periods. In comparison to the multi-model mean, the generalized Bayes method projected a considerably weaker warming during summer and autumn in much of continental Europe, a stronger winter warming in Scandinavia, France, eastern and central Europe, and a weaker warming in both summer and winter in the Mediterranean. These differences can be traced back to the models' difficulties in representing the natural interannual variability in these regions.

Bayesian multi-model projections of climate: generalization and application to ENSEMBLES results