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Atmosphere‐ocean general circulation models (AOGCMs) are useful for assessing the state of the climate atlargescales.Unfortunately,theyarenottractableforthefiner-scaleapplications(e.g.,hydrometeorological variables). Downscalingmethods allowthe transfer of large-scaleinformation tofinerscales andthey are thus relevant for the assessment of finer-scale variables. Among a wide range of downscaling methods, regressionbased approaches are commonly used for downscaling AOGCM data because of their low computational requirements. However, downscaled variables are generally reproduced at gauged weather stations only. Resultsatthegaugedstationscanthenbeinterpolatedaposterioriatungaugedlocationswithkrigingorother methods. In this paper, a spatial Bayesian model is proposed for the downscaling of coarse-scale atmospheric data (i.e., either reanalysis or AOGCM) to minimum and maximum daily temperatures. This approach uses a Bayesian framework for mixing a prior distribution reflecting the monthly spatial dependence of the temperatures with the daily fluctuations induced by the atmospheric predictors. Local characteristics (i.e., altitude and latitude) are also taken into account in the mean of the prior distribution by using a geographical regressionmodel. Theposterior distribution thus reflects bothmonthlylocal patternsbecause ofthe prior and daily larger-scale fluctuations. Finally, the Bayesian approach also allows for the accounting of estimated parameteruncertainty, makingitmore stabletopoorparameterfitting.Themethodisappliedto thesouthern part of the province of Quebec, Canada. Results show that the downscaled distributions of the temperatures at gaugedsitesareinsufficientagreementwith thevalidationdatasetcomparedtoaclassicalregression-based method. The proposed model has also the advantage of directly producing temperature maps.

Spatial Bayesian Model for Statistical Downscaling of AOGCM to Minimum and Maximum Daily Temperatures