A method is presented to produce probability distributions for regional climate change in surface temperature and precipitation. The method combines a probability distribution for global mean temperature increase with the probability distributions for the appropriate scaling variables, i.e., the changes in regional temperature/precipitation per degree global mean warming. The distribution of each scaling variable is assumed to be normal. The uncertainty of the scaling relationship arises from systematic differences between the regional changes from global and regional climate model simulations and from natural variability. The contributions of these sources of uncertainty to the total variance of the scaling variable are estimated from simulated temperature and precipitation data in a suite of regional climate model experiments conducted in the framework of the EU funded project PRUDENCE, using an Analysis Of Variance (ANOVA). Five Case Study Regions (CSRs) are considered: NW England, the Rhine basin, Iberia, Jura lakes (Switzerland) and Mauvoisin dam (Switzerland). These CSRs cover the area considered in the EU funded project SWURVE. The resulting regional climate changes for 2070-2099 vary quite significantly between CSRs, between seasons, and between meteorological variables. A notable point is that for all CSRs the expected warming in summer is higher than the expected warming for the other seasons. This summer warming is accompanied by a large decrease in precipitation. The uncertainty of the scaling ratios for temperature and precipitation is relatively large in summer due to the differences between regional climate models. Differences between the spatial climate-change patterns of global climate model simulations significantly contribute to the uncertainty of the scaling ratio for temperature. However, for the scaling ratio for precipitation sometimes no meaningful contribution could be found due to the small number of global climate models in the PRUDENCE project and the large influence of natural variability. For precipitation, natural variability is often the largest source of uncertainty. By contrast, for temperature the contribution of natural variability to the total variance of the scaling ratio is small, in particular for the annual mean values. Simulation from the probability distributions of global mean warming and the scaling ratio results in a wider range of regional temperature change than that in the regional climate model experiments. For the regional change of precipitation, however, a large proportion of the simulations (about 90%) is within the range of the regional climate model simulations.