Abstract. For understanding past flood changes in the Rhine catchment and in particular for quantifying the role of anthropogenic climate change for extreme flows, an attribution study relying on a proper GCM (General Circulation Model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach given, among others, a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. To test the first requirement, an objective classification scheme is applied and different classification variables, spatial domains and number of classes are evaluated. To this end, 111 years of daily climate data from 500 stations in the Rhine basin are used. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis for a relatively small spatial domain over Central Europe with overall 40 weather type classes is found most appropriate for stratifying six local climate variables. The skill in explaining local climate variability is very different, from high for radiation to low for precipitation. Especially local precipitation and humidity are governed by processes that are not completely represented by the large-scale distribution of pressure, temperature and humidity. Before applying the weather pattern based downscaling approach, it should therefore be investigated whether the link between the large-scale synoptic situation and the local climate variable of interest is strong enough for the given purpose. Our analysis suggests that it is advantageous to incorporate additional classification variables besides pressure fields. The use of temperature results in a very good stratification of weather patterns throughout the year. Hence, there is no need to provide different classifications for each season. To test the skill of the latest generation of GCMs in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs from the CMIP5 ensemble is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis.
Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin