ORIGINAL PAPER Statistical downscaling of daily precipitation over Sweden using GCM output Fredrik Wetterhall & András Bárdossy & Deliang Chen & Sven Halldin & Chong-yu Xu Received: 21 May 2007 / Accepted: 23 April 2008 / Published online: 22 May 2008 # Springer-Verlag 2008 Abstract A classification of Swedish weather patterns (SWP) was developed by applying a multi-objective fuzzy-rule-based classification method (MOFRBC) to large-scale-circulation predictors in the context of statistical downscaling of daily precipitation at the station level. The predictor data was mean sea level pressure (MSLP) and geopotential heights at 850 (H850) and 700 hPa (H700) from the NCEP/NCAR reanalysis and from the HadAM3 GCM. The MOFRBC was used to evaluate effects of two future climate scenarios (A2 and B2) on precipitation patterns on two regions in south-central and northern Sweden. The precipitation series were generated with a stochastic, autoregressive model conditioned on SWP. H850 was found to be the optimum predictor for SWP, and SWP could be used instead of local classifications with little information lost. The results in the climate projection indicated an increase in maximum 5-day precipitation and precipitation amount on a wet day for the scenarios A2 and B2 for the period 2070–2100 compared to 1961–1990. The relative increase was largest in the northern region and could be attributed to an increase in the specific humidity rather than to changes in the circulation patterns. 1 Introduction The increase of greenhouse gases in the atmosphere because of anthropogenic activities is projected to have a large impact on the global climate. A great challenge for the scientific community is to develop methods and models to evaluate the impacts of global climate change at the local scale. General circulation models (GCMs) are useful tools to describe the large-scale dynamics and they are widely used to assess climate change under the assumption of future emission scenarios. However, the models fail to correctly model important parameters for hydrological impact studies such as precipitation and soil moisture (Loaiciga et al. 1996; Wilby and Wigley 1997; Xu 1999a). The main reason for this is that many sub grid- scale processes such as cloud formation, convective rainfall, infiltration, evaporation, and runoff are parameter- ised because of computational limitations and the coarse resolution in GCMs (Zorita and von Storch 1999). GCMs usually model the seasonal variations of precip- itation reasonably well (Johns et al. 2003), but they often do not capture properties that are important for impact studies such as extreme events (Xu 1999b). In the last 10 years, hydrologic schemes in GCMs have been developed with Theor Appl Climatol (2009) 96:95–103 DOI 10.1007/s00704-008-0038-0 F. Wetterhall (*) Swedish Meteorological and Hydrological Institute, Folkborgsvägen 1, 60176 Norrköping, Sweden e-mail: Fredrik.Wetterhall@smhi.se F. Wetterhall : S. Halldin Air and Water Science, Department of Earth Sciences, Uppsala University, Villavägen 16, 75236 Uppsala, Sweden A. Bárdossy Institut fur Wasserbau, Stuttgart University, Pfaffenwaldring 61, 70569 Stuttgart, Germany D. Chen Regional Climate group, Earth Sciences Centre, Göteborg University, Guldhedsgatan 5A, P.O. 460, 40530 Göteborg, Sweden C.-y. Xu Department of Geosciences, University of Oslo, Sem Saelands vei 1, P.O. Box 1047, 0316 Oslo, Norway