Abstract
Abstract. Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from 15 regional climate models (RCMs) from the ENSEMBLES project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and SDMs indicates that at least 30% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an ensemble of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.
Highlights
Both the frequency and intensity of extreme precipitation are expected to increase under climate change conditions in Europe (Christensen and Christensen, 2003; IPCC, 2012)
The main steps often followed in these studies comprise the selection of one or several global climate models (GCMs), regional climate models (RCMs) and/or statistical downscaling methods (SDMs)
This study analyses the expected changes in extreme precipitation in 11 European catchments. It focuses on the variability in the changes arising from the use of different SDMs as well as different RCM–GCM simulations
Summary
Both the frequency and intensity of extreme precipitation are expected to increase under climate change conditions in Europe (Christensen and Christensen, 2003; IPCC, 2012). GCMs are the most comprehensive and widely used models for simulating the response of the global climate system to changes in greenhouse gas emissions. Their spatial resolution (approximately 150 km) is often too coarse for addressing climate change impacts at the local scale, and variables such as precipitation are often biased. RCMs have a higher spatial resolution (often approximately 25 km, but the new EURO-CORDEX simulations (Jacob et al, 2013) have a resolution of approximately 11 km) than GCMs, which makes them more adequate for assessing changes at the local scale. Statistical downscaling is based on defining a relationship between the large-scale outputs of the RCMs (or GCMs) and the local-scale variables required in impact studies (Fowler et al, 2007; Wilby et al, 2004)
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