With increasing evidence of climate change, future decision-making among crop modelers and agronomists will require the inclusion of high-resolution climate predictions from regional climate models as input into agricultural system simulation models to assess the impacts of projected ambient CO2 increases, temperature and general climatic change on crop production. Before they can be implemented in climate adaption studies and decision-support systems, weather variables must be reliable and accurate. This study evaluated weather variables generated from computer simulations using two land surface models, (LSMs) coupled to a regional climate model, namely, Weather Research Forecasting (WRF 3.2). The land surface models tested are the Community Land Surface Model CLM 3.5 and the Noah Land surface model. Ground truth observations from 7 stations in Nebraska from a dry year, a normal year and a wet year (2002, 2005 and 2008 respectively) were used to evaluate the model results. Model results were also compared for their spatial ability to mimic distance-standard error weather variables. Both LSMs performed well in predicting the maximum and minimum temperatures in 2002, 2005 and 2008. Rainfall predictions by both models were not as reliable, based on evaluation for individual stations as well as spatially (state-wide).