Precise forecast of meteorological quantities has always been an important challenge. Direct model outputs (DMO) of Numerical weather prediction models always contain random and systematic errors that reduce the accuracy of predictions. By using post-processing methods on raw output, models can reduce the systematic errors and improve the accuracy of predictions. It has been proven that by using statistical post-processing methods, the skills of the forecasts are mainly improved by reducing systematic errors. In fact, post-processing process, should reduce the systematic error between model predictions and observational values in future by using the statistical relationships between the output of the model and the observations in the past. In this study, the output of the WRF model for the Meteorological Station of Rasht Airport in the period of 8 months for temperature, dew point temperature, maximum temperature and minimum temperature was applied post-processing by the moving average method (MA) and verified. Continuous value verification showed improvement in all quantities, Continuous verification showed improvement in all quantities, and the recovery rate is based on ME from 81% to 110% and based on RSME from 4% to 12%. By defining threshold values, improvements were also observed in most categorical verification values.