This study introduces Classification and Regression Trees (CARTs) as a new tool to explore spatial relationships between different climate patterns in a multimodel ensemble. We demonstrate the potential of CARTs by a simple case study based on time-aggregated patterns of circulation (represented by average levels and variabilities of sea level pressure, SLP) and land surface conditions (diagnosed from the time-averaged surface water balance) from regional climate model simulations (ENSEMBLES) over Europe. These patterns are systematically screened for their relevance to the spatial distribution of persistent hot days. Present-day (ERA40) and future (A1B) climate conditions are analyzed. A CART analysis of the ERA40 reanalysis complements the results for the present-day simulations. In many models, long persistent hot days concur with low variabilities of SLP and high water balance deficits both in present and future. However, for the change patterns (A1B minus ERA40) the analysis indicates that the most robust feature is the link between aggravating persistent hot days and increasing surface water deficits. These results highlight that the factors controlling (in our case spatial) variability are not necessarily the same as those controlling associated climate change signals. Since the analysis yields a rather qualitative output, the model bias problems encountered when studying ensemble averages are alleviated.