Extreme events, including precipitation extremes, can have severe impacts on human society and on ecosystems. In the late 20^th century, heavy precipitation events tend to occur with increasing frequency or proportion to total rainfall over most land areas in the world. Precipitation, however, is the single most difficult variable to simulate in numerical weather/climate models. There are very limited studies on modeling evaluation of long-term trends in extreme precipitation events over the U.S., and even smaller areas within the U.S.. This study examines the downscaling skills of the newly developed regional climate model, the Climate extension of Weather Research and Forecasting Model (CWRF), in long-term trends in high precipitation events (e.g. 75^th, 85^th, 95^th percentiles) over targeted study areas (e.g. the Midwest, Illinois, and Central Illinois) within the U.S. by using Kendall’s tau based slope estimator (Theil-Sen regression). CWRF, with all WRF functionalities for numerical weather prediction (NWP) and enhanced capability for climate applications in the integration of external forcing conditions and physical processes, is driven by the NCEP-DOE AMIP II Reanalysis (R-2), for the period 1982-2008 over the Contiguous United States. All the 27-yr annual and seasonal (warm season: March-August; cold season: September-February) high precipitation events are calculated from the observational data and model outputs. Both the observed and simulated trends for the high precipitation events are calculated by the nonparametric linear regression slope, an alternative to the simple least squares linear regression slope, as well as Kendall’s tau correlation instead of Pearson’s linear correlation coefficient. Rank-based nonparametric significance test, the Mann-Kendall test, is applied to evaluate the statistical significance of the trends in the high precipitation events. This study also has important indications for projections of extreme precipitation events in the future climate.