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[1] This paper compares six statistical downscaling models (SDMs) and three regional climate models (RCMs) in their ability to downscale daily precipitation statistics in a region of complex topography. The six SDMs include regression methods, weather typing methods, a conditional weather generator, and a bias correction and spatial disaggregation approach. The comparison is carried out over the European Alps for current and future (2071-2100) climate. The evaluation of simulated precipitation for the current climate shows that the SDMs and RCMs tend to have similar biases but that they differ with respect to interannual variations. The SDMs strongly underestimate the magnitude of the year-to-year variations. Clear differences emerge also with respect to the year-to-year anomaly correlation skill: In winter, over complex terrain, the better RCMs achieve significantly higher skills than the SDMs. Over flat terrain and in summer, the differences are smaller. Scenario results using A2 emissions show that in winter mean precipitation tends to increase north of about 45°N and insignificant or opposite changes are found to the south. There is good agreement between the downscaling models for most precipitation statistics. In summer, there is still good qualitative agreement between the RCMs but large differences between the SDMs and between the SDMs and the RCMs. According to the RCMs, there is a strong trend toward drier conditions including longer periods of drought. The SDMs, on the other hand, show mostly nonsignificant or even opposite changes. Overall, the present analysis suggests that downscaling does significantly contribute to the uncertainty in regional climate scenarios, especially for the summer precipitation climate.

Abstract. The question whether the magnitude and frequency of floods have changed due to climate change or other drivers of change is of high interest. The number of flood trend studies is rapidly rising. When changes are detected, many studies link the identified change to the underlying causes, i.e. they attribute the changes in flood behaviour to certain drivers of change. We propose a hypothesis testing framework for trend attribution which consists of essential ingredients for a sound attribution: evidence of consistency, evidence of inconsistency, and provision of confidence statement. Further, we evaluate the current state-of-the-art of flood trend attribution. We assess how selected recent studies approach the attribution problem, and to which extent their attribution statements seem defendable. In our opinion, the current state of flood trend attribution is poor. Attribution statements are mostly based on qualitative reasoning or even speculation. Typically, the focus of flood trend studies is the detection of change, i.e. the statistical analysis of time series, and attribution is regarded as an appendix: (1) flood time series are analysed by means of trend tests, (2) if a significant change is detected, a hypothesis on the cause of change is given, and (3) explanations or published studies are sought which support the hypothesis. We believe that we need a change in perspective and more scientific rigour: detection should be seen as an integral part of the more challenging attribution problem, and detection and attribution should be placed in a sound hypothesis testing framework.

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The evolution of daily extreme precipitation and temperature from 1958 to 2001 was investigated within the German side of the Rhine basin. Trends of a set of extreme precipitation and temperature indices defined on daily time series of precipitation and temperature were calculated at 611 precipitation and 232 temperature stations located within the study area and their corresponding significances were tested using the non-parametric Kendall-tau test. The results obtained indicated that both the daily minimum and maximum extreme temperatures have increased over the investigation period, with the degree of change showing seasonal variability. On an annual basis, the change in the daily minimum extreme temperature was found to be greater than that of the daily maximum extreme temperature. The daily extreme heavy precipitation has shown increasing trends both in magnitude and frequency of occurrence in all seasons except summer, where it showed the opposite trend. The station values of the daily precipitation were also interpolated on a regular grid of 5 km × 5 km so that the changes in the indices could be investigated on areal precipitation by aggregating the interpolated precipitation to any desired scale. This enables assessment of the hydrological consequences of the changes in the extreme precipitation. Although the spatial pattern remained more or less similar with that of the point-scale trends for all indices, the average trend magnitude showed an increase with the scale of the area on which precipitation was aggregated.