In mountainous regions, the spatial and temporal distribution of precipitation is strongly influenced by local orography. The resolution of operational numerical weather prediction (NWP) models has been enhanced significantly in recent years but has yet to reach the level necessary to capture fully the influences of high-relief topography on precipitation and flow dynamics. Furthermore, the parameterizations of precipitation mechanisms and cloud microphysics in these models have been developed on the basis of observational and field campaigns generally carried out far away from complex terrain and thus may not represent the physics associated with orographic precipitation. Here we attempt to address both issues by nesting a small-scale physically based orographic precipitation model (OPM) at 1-km resolution within the fifth-generation Penn State/National Center for Atmospheric Research Mesoscale Model (PSU/NCAR MM5) at 12-km resolution. This approach is investigated by simulating six storms in the Pocono Mountains of Pennsylvania. Some improvement over the MM5 was achieved, such as an increase in hourly threat score. The reliability of the MM5 and OPM forecasts for more intense, less frequent events (exceeding 4 mm/h) was shown to be significant, through the threat scores for these amounts indicate a need for additional improvement. This study suggests that further improvements in nested modeling applications are constrained by the degree to which the host model can provide boundary and initial conditions that represent the actual state of the atmosphere. One possible solution for this problem is the adaptive assimilation of remotely sensed data to provide initial and updated moisture and temperature fields throughout a forecast period. ¿ 1999 American Geophysical Union