The separation of mesoscale process and climate variability can be done most conveniently by using time scale; “mesoscale” having a short time scale, shorter than approximately 2-3 days, while “climate” has a long time scale of seasonal average and longer. The mesoscale process may also imply somewhat strong events, such as severe storms, strong winds, large precipitation, or snow and ice events, but also includes weaker phenomena, such as sea and mountain breeze, topographically forced mountain waves and local small scale circulations. The interaction between mesoscale and climate scale may be a self-feedback process, i.e., “climate variability” controls “mesoscale process” but “mesoscale” also controls “climate variability”. An example of the former is the effect of El Nińo/La Nińa which modifies the basic large scale flow in the extra-tropics and associated extra-tropical storm activities which include a variety of mesoscale processes. An example of the latter is not yet systematically documented, but would include elements such as the effect of mountain waves, which is currently parameterized as a gravity wave drag in numerical models. Its long-term effect can be considered as the “mesoscale process” affecting “climate variability”. It is interesting to note that other mesoscale processes, such as mountain-valley and land-sea breeze, strong mesoscale convective systems and many other mesoscale processes are currently not parameterized (cannot be parameterized) in large scale global models, and the only way to simulate these processes is to run the model with very high horizontal resolution. This is one of the major drawbacks of the coarse resolution numerical models most commonly used in climate studies. In this talk, we utilize a recently completed 57-year hourly archive of California region Reanalysis dynamical downscaling at 10km as a data source, and try to see how much the “mesoscale process” affects “climate”. This will be done in several different ways, by examining the variance and EOFs of various fields to identify what are the “typical mesoscale processes” appearing in the long term mean fields. We also calculate various averages with and without “typical mesoscale phenomena” and compare the two to find the importance of “mesoscale” in determining climate mean. We have some preliminary results which suggest that the Santa Ana events in California determine a significant part of the winter climatology over California. We will also conduct a similar study concentrating on other significant weather, such as the pineapple express and possibly the Catalina eddies. We also try to analyze how much the small scale “mesoscale process” contributes to the longer climate time scale, by examining the eddy transport by the small and shorter time scales. Although dynamical downscaling assumes that feedback from small scale to large scale cannot occur, the nudging procedure we introduced into the system to maintain the large scale analysis within the domain seems to allow us to make such diagnostic calculations to estimate the effect of “mesoscale” on climate. In addition to these direct interactions between “mesoscale process” and “climate variability”, long term trend in the number of occurrences and intensity of mesoscale events will be performed to identify possible effects of changing “climate” on “mesoscale processes” in the very long time scale.