The thesis explores an innovative technique for enhancing the precision of short-term weather forecasts, particularly in predicting extreme weather phenomena, which present a notable challenge for existing models such as PySTEPS due to their volatile behavior. Leveraging precipitation and meteorological data sourced from the Royal Netherlands Meteorological Institute (KNMI), the research innovates through the development of a physics-informed neural network. Central to this approach is the implementation of a Physics-Informed Discriminator GAN (PID-GAN), a method that embeds physical principles directly into the adversarial training regime. The architecture is marked by the integration of a Vector Quantization Generative Adversarial Network (VQ-GAN) and a Transformer as the generator, complemented by a temporal discriminator as the discriminator component. Results from this study indicate a notable advancement over traditional numerical weather prediction and cutting-edge deep learning models, underscoring the PID-GAN model's superiority in delivering accurate precipitation nowcasting metrics.