The availability of new climate greenhouse gas scenario data often prompts the question in what respect the new data provide added value with respect to previous versions and whether or not impact models have to be rerun with the new climatic forcing. This question is the case not only for updated sets of underlying climate model ensembles but also for changes in the applied postprocessing method, such as in the upcoming new climate change projection suite CH2018. The new local projection data are no longer post-processed based on the delta change approach but using quantile mapping. Here, we evaluate the added value of new climate projections from a hydrological perspective. We propose an evaluation scheme that comprises both reference and greenhouse gas scenario periods, average values on different temporal aggregation levels, as well as extreme-related multiday indices. For a test catchment (Thur, pre-alpine, 1700 km2) we show that the question about an added value, strongly depends on the variable and aspect (average and extreme) of interest. In many cases, basic hydrological characteristics are similarly represented when employing different climate model postprocessing techniques. However, we found differences in the climate change signal already for mean monthly runoff values and even more for several extreme-related indices. Some of them reveal very similar change signals, while the indices related to the intensity/volume of the extremes can strongly diverge. We argue that the comprehensive comparative analysis presented here is transferable and provides useful guidance for the assessment an added value, especially for climate data providers and impact modellers.
