The National Oceanic and Atmospheric Administration's National Climatic Data Center, to improve the capacity to observe climatic change and variability, is establishing the U.S. Climate Reference Network (CRN). A goal of CRN is to provide long-term homogeneous observations of temperature and precipitation that can be joined with historical observations for detection and attribution of climatic change. The purpose of this study is to estimate the number and distribution of CRN observing stations required to reproduce, within predetermined monitoring goals, the annual temperature and precipitation variability across the United States. The approach taken is a lower-resolution resampling of reference or baseline networks. Monte Carlo sampling techniques are used to choose stations within 2.5 degrees latitude by 3.5 degrees longitude grid cells. Measures of similarity (e.g., mean-absolute deviation) between lower resolution networks and baseline networks are generated for each grid cell. Thus the grid cell densities required to meet pre-determined network monitoring goals can be determined in a manner that reflects the climate variability from region to region. This process can be repeated for both temperature and precipitation. By summing over all grid cells, the number of stations required for the national network can be quantified.