In the present study, nonstationarities in predictor–predictand relationships within the framework of statistical downscaling are investigated. In this context, a novel validation approach is introduced in which nonstationarities are explicitly taken into account. The method is based on results from running calibration periods. The (non)overlaps of the bootstrap confidence interval of the mean model performance (derived by averaging the performances of all calibration/verification periods) and the bootstrap confidence intervals of the individual model errors are used to identify (non)stationary model performance. The specified procedure is demonstrated for mean daily precipitation in the Mediterranean area using the bias to assess model skill. A combined circulation-based and transfer function–based approach is employed as a downscaling technique. In this context, large-scale seasonal atmospheric regimes, synoptic-scale daily circulation patterns, and their within-type characteristics, are related to daily station-based precipitation. Results show that nonstationarities are due to varying predictors–precipitation relationships of specific circulation configurations. In this regard, frequency changes of circulation patterns can damp or increase the effects of nonstationary relationships. Within the scope of assessing future precipitation changes under increased greenhouse warming conditions, the identification and analysis of nonstationarities in the predictors–precipitation relationships leads to a substantiated selection of specific statistical downscaling models for the future assessments. Using RCP4.5 scenario assumptions, strong increases of daily precipitation become apparent over large parts of the western and northern Mediterranean regions in winter. In spring, summer, and autumn, decreases of precipitation until the end of the 21st century clearly dominate over the entire Mediterranean area.