The fifth-generation PSU - NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) capability in simulating the U. S. precipitation annual cycle is evaluated with a 1982 - 2002 continuous baseline integration driven by the NCEP - DOE second Atmospheric Model Intercomparison Project ( AMIP II) reanalysis. The causes for major model biases ( differences from observations) are studied through supplementary seasonal sensitivity experiments with various driving lateral boundary conditions (LBCs) and physics representations. It is demonstrated that the CMM5 has a pronounced rainfall downscaling skill, producing more realistic regional details and overall smaller biases than the driving global reanalysis. The precipitation simulation is most skillful in the Northwest, where orographic forcing dominates throughout the year; in the Midwest, where mesoscale convective complexes prevail in summer; and in the central Great Plains, where nocturnal low-level jet and rainfall peaks occur in summer. The actual model skill, however, is masked by existing large LBC uncertainties over data-poor areas, especially over oceans. For example, winter dry biases in the Gulf States likely result from LBC errors in the south and east buffer zones. On the other hand, several important regional biases are identified with model physics deficiencies. In particular, summer dry biases in the North American monsoon region and along the east coast of the United States can be largely rectified by replacing the Grell with the Kain - Fritsch cumulus scheme. The latter scheme, however, yields excessive rainfall in the Atlantic Ocean but large deficits over the Midwest. The fall dry biases over the lower Mississippi River basin, common to all existing global and regional models, remain unexplained and the search for their responsible physical mechanisms will be challenging. In addition, the representation of cloud - radiation interaction is essential in determining the precipitation distribution and regional water recycling, for which the new scheme implemented in the CMM5 yields significant improvement.
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