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With the rapid development of the civil aviation industry, flight delays caused by convective weather are becoming increasingly severe. In terminal airspace with complex traffic environments, these delays can propagate to subsequent arrivals and departures, thereby affecting the operational efficiency of the terminal airspace and potentially disrupting the entire air traffic system. Accurate short-term forecasting of convective weather can help reduce flight delays, improve airspace utilization, and decrease the workload of air traffic controllers. This study, grounded in supervised deep learning, addresses civil aviation operational requirements by developing a short-term forecasting model for convective weather based on a Convolutional Neural Network-Transformer (CNN-Transformer). The model leverages eight types of weather products from civil aviation weather radar and ERA5, including basic reflectance, echo top, vertically integrated liquid, relative humidity, temperature, U-component of wind, V-component of wind, and vertical velocity. The training was conducted using 241 datasets, totaling 23,881 samples. To evaluate the model’s validity, ablation studies were performed on each parameter, and its performance was compared with the Centroid Method, Optical Flow, CNN-CNN, and CNN-Long Short-Term Memory (CNN-LSTM). According to six evaluation indicators, traditional radar echo extrapolation showed better forecast accuracy within 1 hour, while the CNN-Transformer-based short-term forecasting model for convective weather demonstrated superior performance for 2-6 hour forecasts.