<p>Precipitation nowcasting is of great importance for disaster prevention and mitigation. However, precipitation is a complex spatio-temporal phenomenon influenced by various underlying physical factors. Even slight changes in the initial precipitation field can have a significant impact on the future precipitation patterns, making the nowcasting of short-term high-resolution precipitation a major challenge. Traditional deep learning methods often have difficulty capturing the long-term spatial dependence of precipitation and are usually at a low resolution. To address these issues, based upon the Simpler yet Better Video Prediction (SimVP) framework, we proposed a deep generative neural network that incorporates the Simple Parameter-Free Attention Module (SimAM) and Generative Adversarial Networks (GANs) for short-term high-resolution precipitation event forecasting. Through an adversarial training strategy, critical precipitation features were extracted from complex radar echo images. During the adversarial learning process, the dynamic competition between the generator and the discriminator could continuously enhance the model in prediction accuracy and resolution for short-term precipitation. Experimental results demonstrate that the proposed method could effectively forecast short-term precipitation events on various scales and showed the best overall performance among existing methods.</p>