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Abstract A statistical downscaling approach for precipitation in France based on the analog method and its evaluation for different combinations of predictors is described, with focus on the transferability of the method to the future climate. First, the realism of downscaled present‐day precipitation climatology and interannual variability for different combinations of predictors from four reanalyses is assessed. Satisfactory results are obtained, but elaborated predictors do not lead to major and consistent across‐reanalyses improvements. The downscaling method is then evaluated on its capacity to capture precipitation trends in the last decades. As uncertainties in downscaled trends due to the choice of the reanalysis are large and observed trends are weak, this analysis does not lead to strong conclusions on the applicability of the method to a changing climate. The temporal transferability is then assessed thanks to a perfect model framework. The statistical downscaling relationship is built using present‐day predictors and precipitation simulated by 12 regional climate models. The entire projections are then downscaled, and future downscaled and simulated precipitation changes are compared. A good temporal transferability is obtained only with a specific combination of predictors. Finally, the regional climate models are downscaled, thanks to the relationship built with reanalyses and observations, for the best combination of predictors. Results are similar to the changes simulated by the models, which reinforces our confidence in the realism of the models and of the downscaling method. Uncertainties in precipitation change due to reanalyses are found to be limited compared to those due to regional simulations.
Dayon et al. (Wed,) studied this question.