precipitation forecasting is critical due to its use in flood and water resources studies. Numerical weather predictions have been extensively developed in recent years. Today, the world's leading meteorological centers have used these models in their meteorological forecasts. The TIGGE database (dataset) uses a number of these centers in its meteorological forecasts. The present study was conducted to evaluate the performance of numerical models (ECMWF, MeteoFrance, NCEP) available in the database to predict precipitation in the Poldakhtar basin. Also, the effect of bias correction methods (EZ, QM, Delta) on precipitation forecasting models is another goal of this research. The obtained results showed that most prediction models had the lowest RMSE at low altitudes and the highest value at high altitudes. Among the models used in this study, the ECMWF model had the lowest RMSE in other parts of the Poldakhtar basin compared to other models, while the NCEP model had the highest RMSE value. Also, the evaluation of the corrected precipitation showed that the EZ method was the best oblique correction method in the proximity of the predicted precipitation to the observed data. In contrast, the Delta method was superior in RMSE and CC index. However, all bias correction methods have improved the predicted precipitation values to an acceptable level, which increases the efficiency of the precipitation prediction models in the flood warning system.