Regional Climate Models (RCMs) constitute the most often used method to perform affordable highresolution regional climate simulations. The key issue in the evaluation of nested regional models is to determine whether RCM simulations improve the representation of climatic statistics compared to the driving data, that is, whether RCMs add value. In this study we examine a necessary condition that some climate statistics derived from the precipitation field must satisfy in order that the RCM technique can generate some added value: we focus on whether the climate statistics of interest contain some fine spatial-scale variability that would be absent on a coarser grid. The presence and magnitude of fine-scale precipitation variance required to adequately describe a given climate statistics will then be used to quantify the potential added value (PAV) of RCMs. Our results show that the PAV of RCMs is much higher for short temporal scales (e.g., 3-hourly data) than for long temporal scales (16-day average data) due to the filtering resulting from the time-averaging process. PAV is higher in warm season compared to cold season due to the higher proportion of precipitation falling from small-scale weather systems in the warm season. In regions of complex topography, the orographic forcing induces an extra component of PAV, no matter the season or the temporal scale considered. The PAV is also estimated using high-resolution datasets based on observations allowing the evaluation of the sensitivity of changing resolution in the real climate system. The results show that RCMs tend to reproduce relatively well the PAV compared to observations although showing an overestimation of the PAV in warm season and mountainous regions.

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To study the internal variability of the model and its consequences on seasonal statistics, large ensembles of twenty 3-month simulations of the Canadian Regional Climate Model (CRCM), differing only in their initial conditions, were generated over different domain sizes in eastern North America for a summer season. The degree of internal variability was measured as the spread between the individual members of the ensemble during the integration period. Results show that the CRCM internal variability depends strongly on synoptic events, as is seen by the pulsating behavior of the time evolution of variance during the period of integration. The existence of bimodal solutions for the circulation is also noted. The geographical distribution of variance depends on the variables; precipitation shows maximum variance in the southern United States, while 850-hPa geopotential height exhibits maximum variance in the northeast part of the domain. Results suggest that strong precipitation events in the southern United States may act as a triggering mechanism for the 850-hPa geopotential height spread along the storm track, which reaches its maximum toward the northeast of the domain. This study reveals that successive reductions of the domain size induce a general decrease in the internal variability of the model, but an important variation in its geographical distribution and amplitude was detected. The influence of the internal variability at the seasonal scale was evaluated by computing the variance between the individual member seasonal averages of the ensemble. Large values of internal variability for precipitation suggest possible repercussions of internal variability on seasonal statistics.