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Statistical downscaling of GCM simulations is widely used for examining future changes in precipitation at different spatial and temporal scales. However, the downscaling process is affected by uncertainty associated with the downscaling model, its parameters, and also the use of different reanalysis products for model calibration. This study develops a Bayesian approach to calibrating a statistical downscaling model. The study investigates the impact of using two different reanalysis products, the National Centre for Environmental Prediction/National Centre for Atmospheric Research Reanalysis 2 (NCEP2) and the European Centre for Medium-Range Forecasts Interim Reanalysis (ERAI), in precipitation downscaling over the Tibetan plateau, a region with sparse ground precipitation coverage. The selected reanalysis products are used for modelling precipitation at selected locations with long, high quality records and diverse geographic characteristics. An assessment of the downscaled precipitation results using atmospheric variables from the ACCESS 1.3 GCM to drive the downscaling model calibrated using a reanalysis dataset is also performed and the impact of calibration uncertainty quantified. The outcomes of this study reveal that the choice of the data length used and the type of Reanalysis product adopted have a significant effect in downscaled precipitation characteristics and their uncertainties, such as the wetness fraction and average annual precipitations over the study locations. These findings point to a common problem in statistical downscaling applications and one that has not been recognised until now. The results show that downscaling model considering ERAI reproduce observed precipitation attributes to a better extent than NCEP2.