Abstract: This work presents a comprehensive assessment of the suitability of random forests, a well-known machine learning technique, for the statistical downscaling of precipitation. Building on the experimental and validation framework proposed in the Experiment 1 of the COST action VALUE-the largest, most exhaustive intercomparison study of statistical downscaling methods to date-we introduce and thoroughly analyze a posteriori random forests (AP-RFs), which use all the information contained in the leaves to reliably predict the shape and scale parameters of the gamma probability distribution of precipitation on wet days. Therefore, as opposed to traditional random forests, which typically provide deterministic predictions, our AP-RFs allow realistic stochastic precipitation samples to be generated for wet days. Indeed, as compared to one particular implementation of a generalized linear model that exhibited an overall good performance in VALUE, our AP-RFs yield better distributional similarity with observations without loss of predictive power. Noteworthy, the new methodology proposed in this paper has substantial potential for hydrologists and other impact communities which are in need of local-scale, reliable stochastic climate information.

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