In the Great Plains of the United States, the low-level jet (LLJ) is a critical part of the regional hydroclimate because of its role in producing precipitation necessary for agriculture. Mesoscale convective systems are the dominant mode of precipitation during the warm season (April-August), and the moisture necessary to sustain these systems is transported northward from the Gulf of Mexico by the LLJ. However, model uncertainty exists in the resolution of the location, frequency and intensity of the LLJ. To quantify this uncertainty, we diagnosed characteristics of the LLJ based on criteria proposed by Bonner (1968). We further apply this metric to a set of dynamically downscaled simulations from two regional climate models, WRF-ARW and RegCM4, with varying horizontal grid resolutions (50km, 25km, and 12km). The models are driven by a reanalysis dataset (ERA-Interim) and three global climate models (GFDL-ESM2M, HadGEM2-ES, MPI-ESM-LR), in conjunction with CORDEX-North America and the DOE FACETS projects. Results show that the LLJ is under predicted in the historical climate simulations with each combination of global model, regional model and grid resolution. This presentation will explore why there is bias in the downscaled simulations, and how this uncertainty affects moisture transport and precipitation in the Great Plains.