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This study presents a statistical downscaling (SD) method for high-altitude,glaciated mountain ranges. The SD method uses an a priori selectionstrategy of the predictor (i.e., predictor selection without data analysis).In the SD model validation, emphasis is put on appropriately considering thepitfalls of short observational data records that are typical of highmountains. An application example is shown, with daily mean air temperaturefrom several sites (all in the Cordillera Blanca, Peru) as target variables,and reanalysis data as predictors. Results reveal strong seasonal variationsof the predictors' performance, with the maximum skill evident for the wet(and transitional) season months January to May (and September), and thelowest skill for the dry season months June and July. The minimum number ofobservations (here, daily means) required per calendar month to obtainstatistically significant skill ranges from 40 to 140. With increasing dataavailability, the SD model skill tends to increase. Applied to a choice ofdifferent atmospheric reanalysis predictor variables, the presented skillassessment identifies only air temperature and geopotential height assignificant predictors for local-scale air temperature. Accounting fornatural periodicity in the data is vital in the SD procedure to avoidspuriously high performances of certain predictors, as demonstrated here fornear-surface air temperature. The presented SD procedure can be applied tohigh-resolution, Gaussian target variables in various climatic andgeo-environmental settings, without the requirement of subjectiveoptimization

A statistical downscaling method for daily air temperature in data-sparse, glaciated mountain environments