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A 2-step downscaling method for general circulation model (GCM) outputs is presented. The investigation is based on daily upper air geopotential and humidity fields (Atlantic-European sector) and surface observations (52 German chmate stations) from 1966 to 1993. In the first step, significant circulation patterns are identified using cluster analysis which takes advantage of Information exclusively from upper air fields. This leads to composite charts which can be readily interpreted synoptically, but which only explain a portion of the variance of the local weather elements. In the second step, a conditional (weather pattern-dependent) stepwise screening regression analysis is performed for each weather element and 6 German climate regions A principal finding is that the modelled (downscaled) local c h a t e is in good agreement with observations, particularly for the temperature regime, due to the fact that a major part of the variance is explained after the 2 steps. Including 700 hPa humidity slightly improves the explained variance. An application to a CCM control run is added. It shows that the method is capable of reconstructing interannual variability of local weather elements.

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In this paper, the weather generator (WG) used by the empirical statistical downscaling method WETTREG (weather situation-based regionalization method (in German: WETTerlagen-basierte REGionalisierungsmethode)), is described. It belongs to the class of multi-site parametric models that aim at the representation of the spatial dependence among weather variables with conditioning on exogenous atmospheric predictors. The development of the WETTREG WG was motivated by (i) the requirement of climate impact modelers to obtain input data sets that are consistent and can be produced in a relatively economic way and (ii) the well-sustained hypothesis that large scale atmospheric features are well reproduced by climate models and can be used as a link to regional climate. The WG operates at daily temporal resolution. The conditioning factor is the temporal development of the frequency distribution of circulation patterns. Following a brief description of the strategy of classifying circulation patterns that have a strong link to regional climate, the bulk of this paper is devoted to a description of the WG itself. This includes aspects, such as the utilized building blocks, seasonality or the methodology with which a signature of climate change is imprinted onto the generated time series. Further attention is given to particularities of the WG's conditioning processes, as well as to extremes, areal representativity and the interface of WGs and user requirements.

When assessing the magnitude of climate signals in a regional scale, a host of optional approaches is feasible. This encompasses the use of regional climate models (RCM), nested into global climate models (GCM) for an area of interest as well as employing empirical statistical downscaling methods (ESD). In this context the question is addressed: Is an empirical statistical downscaling method capable of yielding results that are comparable to those by dynamical RCMs? Based on the presented ESD method, the comparison of RCM and ESD results show a high amount of agreement. In addition the empirical statistical downscaling can be applied directly to a GCM or a GCM-RCM cascade. The paper aims at comparing the consequences of employing various CGM-RCM-ESD combinations on the derived future changes of temperature and precipitation. This adds to the insight on the scale dependency of downscaling strategies. Results for one GCM with several scenario runs driving several RCMs with and without subsequent empirical statistical downscaling are presented. It is shown that there are only small differences between using the GCM results directly or as a GCM-RCM-ESD cascade.