A comparison of different regional climate models and statistical downscaling methods for extreme rainfall estimation under climate change

In most cases climate change projections from General Circulation Models (GCM) and Regional Climate Models ( RCM) cannot be directly applied to climate change impact studies, and downscaling is therefore needed. A large number of statistical downscaling methods exist but no clear recommendations exist of which methods are more appropriate, depending on the application. This paper compares five statistical downscaling methods based on a common change factor methodology using results from four different RCMs driven by different GCMs. Precipitation time series for a future scenario are generated for a location north of Copenhagen for the period 2071-2100 under climate change projections by the scenario A1B. Special focus is given to the changes of extreme events since downscaling methods mainly differ in the way extreme events are generated. There is a significant uncertainty in the downscaled projected changes of the mean, standard deviation, skewness and probability of dry days. Large uncertainties are also observed in the downscaled changes in extreme event statistics. However, three of the four RCMs analysed show an increase in the extreme precipitation events in the future. The uncertainties are partly due to the variability of the RCM projections and partly due to the variability of the statistical downscaling methods. The paper highlights the importance of acknowledging the limitations and advantages of different statistical downscaling methods as well as the uncertainties in downscaling climate change projections for use in hydrological models. (C) 2011 Elsevier B.V. All rights reserved.