![]() 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 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 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). 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. ![]()
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