In Part I of the book, Chapter 5, we discussed that climate science is post-normal,“where the stakes are high, uncertainties large and decisions urgent, and where values are embedded in the way science is done and spoken” (Hulme 2007). This arena has lead to a serious imbalance of fundamental science and application. Delivery of actionable products is requested from scientists and often promised by eager scientists, where in many cases not even credibility has been established.

In fact, a recurrent theme of this book is that projecting regional climate change is indeed, as stated by Hewitson et al. (2014), still a matter of fundamental research. An often-repeated commonplace in statistical downscaling research is that no single method is applicable to all user problems. This vague statement might leave a user frustrated, and browsing through this book, one might get a similar impression. But is it nothing that we have learned, as sometimes claimed? Do we have to give ourselves to magic art, as Faust did? We believe that in many cases robust answers may be given already. In those cases, where more research is needed, a precise list of research questions exists; answering them may bring us a leap forward. The main purpose of this chapter is to summarise these questions.

Initially, let us revisit the key messages from the book. Downscaling is not always meaningless; it may add substantial value, and it may be crucial to provide credible information about regional climate change. Downscaling is highly uncertain if local climate change is dominated by changes in the large-scale circulation. In fact, largescale circulation errors of dynamical models cannot be overcome by any statistical postprocessing and strongly limit the use of downscaling. But downscaling has the potential for added value if circulation errors are moderate, in particular in complex terrain. And we know quite precisely which type of downscaling approach and method is suitable in which situation. In fact, where local feedbacks or small-scale processes are important, dynamical downscaling might be required.