<p>Numerical Weather Prediction models (NWP) are atmospheric simulations that imitate the dynamics of the atmosphere and provide high-quality forecasts. One of the most significant limitations of NWP is the elevated amount of computational resources required for its functioning, which limits the spatial and temporal resolution of the outputs. Traditional meteorological techniques to increase the resolution are based uniquely on information from a limited group of interest variables. In this study, we offer an alternative approach to the task where we generate precipitation maps based on the complete set of variables of the NWP to generate high-resolution and short-time precipitation predictions. To achieve this, five different deep learning models were trained and evaluated: baseline, U-Net, two deconvolutional networks, and one conditional generative model (CGAN). A total of 20 independent random initializations were performed for each of the models. The predictions were evaluated using MAE and LEPS-based skill scores, ETS, CSI, and frequency bias after applying several thresholds. The models showed a significant improvement in predicting precipitation showing the benefits of including the complete information from the NWP. The algorithms increased the resolution of the predictions and corrected an over-forecast bias from the input information. However, some new models presented new types of bias: U-Net tended to mid-range precipitation events, and the deconvolutional models favored low rain events and generated some spatial smoothing. The CGAN offered the highest quality precipitation forecast generating realistic outputs and indicating possible future research paths.</p>