Precipitation occurrence is influenced by numerous meteorological variables, making its prediction a complex task. Consequently, both numerical weather prediction (NWP) models and statistical approaches have been developed to forecast daily precipitation. One promising method for improving forecast accuracy involves the use of Artificial Neural Networks (ANNs), which can be employed either directly for precipitation prediction or for post-processing the outputs of NWP models. In this study, meteorological variables derived from the output of the WRF (Weather Research and Forecasting) model were utilized to enhance hourly and daily precipitation forecasts. Specifically, an ANN-based post-processing approach was applied to refine the WRF model outputs. The meteorological variables extracted hourly over a three-year period (2021–2023) included accumulated precipitation (APCP), dew point temperature (DPT), mean sea-level pressure (PRMSL), relative humidity at 2 meters (RH2), 2-meter air temperature (T2m), and wind speed (Wspeed). These variables were categorized into forecast lead times of 6, 12, 18, 24, 48, and 72 hours, with each lead time used independently as input to the ANN model for each station.The ANN architecture employed was a multilayer perceptron (MLP) consisting of two layers: a hidden layer with sigmoid activation functions and an output layer with linear activation. The model was trained to predict 6-hour observed precipitation at each station. The ANN was implemented across 16 synoptic meteorological stations in East Azerbaijan Province. Model performance was evaluated using statistical metrics including Mean Squared Error (MSE) and Pearson correlation coefficient (R), calculated for the training, testing, and validation datasets. The model was iteratively trained until the minimum MSE and maximum R values were achieved. Results indicated that the ANN-enhanced WRF model significantly improved precipitation forecasts, with MSE reductions ranging from 36% to 92% depending on the station. Also, the correlation index R for all stations was 0.85 on average. Furthermore, 95% daily error plots demonstrated that the modified model exhibited lower error magnitudes and reduced variability compared to the original WRF outputs.