Volunteers contributing idle computing time are helping to create an unprecedented combination of high-spatial and high-statistical resolution in simulations of climate in the C limate system modeling has made tremendous advancements in recent decades. Rapidly expanding computational capabilities and scientific research on fundamental processes have allowed simultaneous progress on a variety of fronts, such as expansion of the processes represented in climate models including interactive carbon cycles represented by biogeochemical models (e.g., Flato 2011), increases in spatial resolution (global models now providing century-long runs at grid spacing as low as ~50 km), and the number of simulations possible with a given model.One area of research currently at the crossroads of basic research and applications is the description of present and future climate at spatial scales that are meaningful both scientifically and for management applications (e.g., Means et al. 2010). Regional climate models (RCMs; e.g., Giorgi 1990) have been implemented over specific areas of interest with resolutions as high as 500 m (Wang et al. 2013) compared to 50-300 km for a GCM. Typically, such studies run the RCM one or at most a handful of times. The problem with having a very small number of simulations is that differences between past and future simulations can stem from several sources, not just the change in greenhouse gases: uncertainty is not well quantified. As O'Brien et al. (2011) note, some studies tacitly assume "that differences between model simulations are entirely due to a physical forcing" and show that internal variability can be larger than the signal in some instances; they