Numerical weather prediction (NWP) models are one of the most important tools that meteorologists use to forecast weather. However, due to shortcomings in the model formulation, numerics and uncertainty in the initial state of the atmosphere, the forecasts of NWP models contain errors. For example, the maximum temperature forecasts produced by NWP models for Oman are not as accurate as one would require. In this project, we use some known calibration techniques such as the classical, inverse regression, and distributed lag methods to improve the accuracy of the NWP temperature forecasts for Seeb station. The results from the different calibration methods are compared. All the calibration methods used in this project have shown a significant improvement over the NWP forecasts. Although there was a little variation in the performance of the three methods, the best calibration results were obtained through the distributed lag model. The inverse approach was ranked second best, followed by the classical method. Key words: Calibration, Linear Regression, Inverse Regression, Frequentist Approach, Bayesian Approach, Classical Estimator, Inverse Estimator, Distributed Lag Model, Numerical Weather Prediction Model, Model Output Statistics (MOS), Paired T-Test, Confidence Interval, P-value.