| description abstract | Statistical downscaling methods and potential large-scale predictors are intercompared for winter daily mean temperature in a network of stations in central and western Europe. The methods comprise (i) canonical correlation analysis (CCA), (ii) singular value decomposition analysis, (iii) multiple linear regression (MLR) of predictor principal components (PCs) with stepwise screening, (iv) MLR of predictor PCs without screening (i.e., all PCs are forced to enter the regression model), and (v) MLR of gridpoint values with stepwise screening (pointwise regression). The potential predictors include two circulation variables (sea level pressure and 500-hPa heights) and two temperature variables (850-hPa temperature and 1000?500-hPa thickness). The methods are evaluated according to the accuracy of specification (in terms of rmse and variance explained), their temporal structure (characterized by lag-1 autocorrelations), and their spatial structure (characterized by spatial correlations and objectively defined divisions into homogeneous regions). The most accurate specification and best approximation of the temporal structure are achieved by the pointwise regression; the spatial structure is best captured by CCA. The best choice of predictors appears to be a pair of one circulation and one temperature predictor. Of the two ways of reproducing the original variance, inflation yields more realistic both temporal and spatial variability than randomization. The size of the domain on which predictors are defined plays a rather negligible role. | |
