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The heterogeneity of precipitation rates in high-mountain regions is notsufficiently captured by state-of-the-art climate reanalysis products due totheir limited spatial resolution. Thus there exists a large gap between theavailable data sets and the demands of climate impact studies. The presentedapproach aims to generate spatially high resolution precipitation fields fora target area in central Asia, covering the Tibetan Plateau and the adjacentmountain ranges and lowlands. Based on the assumption that observed local-scale precipitation amounts are triggered by varying large-scale atmosphericsituations and modified by local-scale topographic characteristics, thestatistical downscaling approach estimates local-scale precipitation ratesas a function of large-scale atmospheric conditions, derived from theERA-Interim reanalysis and high-resolution terrain parameters. Since therelationships of the predictor variables with local-scale observations arerather unknown and highly nonlinear, an artificial neural network (ANN) wasutilized for the development of adequate transfer functions. DifferentANN architectures were evaluated with regard to their predictive performance.  The final downscaling model was used for the cellwise estimation of monthlyprecipitation sums, the number of rainy days and the maximum dailyprecipitation amount with a spatial resolution of 1 km<sup>2</sup>. Themodel was found to sufficiently capture the temporal and spatial variationsin precipitation rates in the highly structured target area and allows for adetailed analysis of the precipitation distribution. A concludingsensitivity analysis of the ANN model reveals the effect of the atmosphericand topographic predictor variables on the precipitation estimations in theclimatically diverse subregions

Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach