VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. In this paper, we present the key ingredients of this framework. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.

In climate change research ensembles of climate simulations are produced in an attempt to cover the uncertainty in future projections. Many climate change impact studies face difficulties using the full number of simulations available, and therefore often only subsets are used. Until now such subsets were chosen based on their representation of temperature change or by accessibility of the simulations. By using more specific information about the needs of the impact study as guidance for the clustering of simulations, the subset fits the purpose of climate change impact research more appropriately. Here, the sensitivity of such a procedure is explored, particularly with regard to the use of different climate variables, seasons, and regions in Europe. While temperature dominates the clustering, the resulting selection is influenced by all variables, leading to the conclusion that different subsets fit different impact studies best. (C) 2016 The Authors. Published by Elsevier Ltd.