Grids containing monthly normal maximum and minimum temperatures were created by using Geographic Information Systems (GIS) techniques which aided in combining data from multiple sources and formatting the data to match the appropriate grid characteristics. The grids are 30-year climatologies obtained from merging Oregon State University's Parameter-elevation Regressions on Independent Slopes Model (PRISM) data spanning the years 1971 2000 over the CONUS and the University Corporation for Atmospheric Research's (UCAR) International Comprehensive Ocean Atmosphere Data Set (ICOADS), which provided data spanning the same time frame over the oceans and the Great Lakes. The output GIS data were then used to create normal temperatures every fifth day by applying a cubic spline interpolation algorithm to fit the monthly normals and then interpolate to the appropriate day. These gridded datasets will be used as predictors in the gridded MOS forecast guidance for temperatures, which is derived from the National Centers for Environmental Prediction's (NCEP) Global Forecast System (GFS) numerical weather prediction model. This presentation focuses on the techniques used to generate the climatological datasets, as well as their importance to the gridded MOS system.