The analysis used hourly forecasts from July 2015 through July 2016 from several hundred observing locations across the continental United States. Several methods of deriving a daily and overnight temperature were created and tested against METAR observations. Three methods used only the forecasted hourly temperatures: (1) a “bias” adjustment from the high (low), (2) a decision tree with adjustments that depended on time of day and forecast temperature range, and (3) extensions that included both linear and nonlinear functions. Additionally, the use of forecasted values of non-temperature variables were tested and evaluated. Skill was assessed through several metrics including bias calculation, root mean squared error, and percent within plus or minus 3°F. Preliminary results indicate that the more complex methods can provide noticeable improvement over simple “bias” adjustments to the predicted hourly time series.