This study deals with an analysis of the performance of a general circulation model (GCM) (HadCM2) in reproducing the large-scale circulation mechanisms controlling Swedish precipitation variability, and in estimating regional climate changes owing to increased CO2 concentration by using canonical correlation analysis (CCA). Seasonal precipitation amounts at 33 stations in Sweden over the period 1899–1990 are used. The large-scale circulation is represented by sea level pressure (SLP) over the Atlantic–European region.
The link between seasonal Swedish precipitation and large-scale SLP variability is strong in all seasons, but especially in winter and autumn. For these two seasons, the link is a consequence of the North Atlantic Oscillation (NAO) pattern. In winter, another important mechanism is related to a cyclonic/anticyclonic structure centred over southern Scandinavia. In the past century, this connection has remained almost unchanged in time for all seasons except spring. The downscaling model that is built on the basis of this link is skilful in all seasons, but especially so in winter and autumn. This observed link is only partially reproduced by the HadCM2 model, while large-scale SLP variability is fairly well reproduced in all seasons. A concept about optimum statistical downscaling models for climate change purposes is proposed. The idea is related to the capability of the statistical downscaling model to reproduce low frequency variability, rather than having the highest skill in terms of explained variance. By using these downscaling models, it was found that grid point and downscaled climate signals are similar (increasing precipitation) in summer and autumn, while in winter, the amplitudes of the two signals are different. In spring, both signals show a slight increase in the northern and southern parts of Sweden. Copyright © 2001 Royal Meteorological Society