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. Stochastic rainfall downscaling methods usually do not take intoaccount orographic effects or local precipitation features at spatial scalesfiner than those resolved by the large-scale input field. For this reasonthey may be less reliable in areas with complex topography or with sub-gridsurface heterogeneities. Here we test a simple method to introduce realisticfine-scale precipitation patterns into the downscaled fields, with theobjective of producing downscaled data more suitable for climatological andhydrological applications as well as for extreme event studies. The proposedmethod relies on the availability of a reference fine-scale precipitationclimatology from which corrective weights for the downscaled fields arederived. We demonstrate the method by applying it to the Rainfall FilteredAutoregressive Model (RainFARM) stochastic rainfall downscaling algorithm. The modified RainFARM method is tested focusing on an area of complextopography encompassing the Swiss Alps, first, in a “perfect-modelexperiment” in which high-resolution (4 km) simulations performed with theWeather Research and Forecasting (WRF) regional model are aggregated to acoarser resolution (64 km) and then downscaled back to 4 km and compared withthe original data. Second, the modified RainFARM is applied to the E-OBSgridded precipitation data (0.25 ∘ spatial resolution) over Switzerland,where high-quality gridded precipitation climatologies and accurate in situobservations are available for comparison with the downscaled data for theperiod 1981–2010. The results of the perfect-model experiment confirm a clear improvementin the description of the precipitation distribution when the RainFARMstochastic downscaling is applied, either with or without the implementedorographic adjustment. When we separately analyze grid points withprecipitation climatology higher or lower than the median calculated over theneighboring grid points, we find that the probability density function (PDF)of the real precipitation is better reproduced using the modified RainFARMrather than the standard RainFARM method. In fact, the modified methodsuccessfully assigns more precipitation to areas where precipitation is onaverage more abundant according to a reference long-term climatology. The results of the E-OBS downscaling show that the modified RainFARMintroduces improvements in the representation of precipitation amplitudes.While for low-precipitation areas the downscaled and the observed PDFs are ingood agreement, for high-precipitation areas residual differences persist,mainly related to known E-OBS deficiencies in properly representing thecorrect range of precipitation values in the Alpine region. The downscalingmethod discussed is not intended to correct the bias which may be present inthe coarse-scale data, so possible biases should be adjusted before applyingthe downscaling procedure.

Stochastic downscaling of precipitation in complex orography: a simple method to reproduce a realistic fine-scale climatology