Dynamical downscaling (DDS) is performed using regional climate models (RCMs) with global atmospheric states as the input, but there is no consensus among researchers on how to define and estimate the resolvable scale of the various climatic variables obtained by DDS. Sources of RCM uncertainties, including both internal model and intermodel variability, have been assessed by performing ensemble simulations and model intercomparisons, sometimes under the controversial assumption that model bias is independent of the climatic state. Compared with low-resolution global climate simulations, DDS can add value in several ways. For example, because they consider high-resolution topographic data, RCMs can often capture mesoscale phenomena and can better represent climate dynamics. Another downscaling method, empirical statistical downscaling (ESD), is complementary to DDS because it is based on a different philosophy (i.e., sources of information) and on a mostly different set of assumptions. More collaboration and communication should be encouraged among those who develop models, those who use models and perform downscaling, those who use downscaling data, and those who make decisions based on the scientific results provided by models. In addition, ensemble experiments should be devised that can more effectively benefit impact studies. Using DDS and ESD, separately or in combination, users can maximize the utility of local climate information.