Abstract: Lightning exhibits significant spatial variability, sudden occurrence, and rapid evolution with short life cycles, making high-resolution nowcasting challenging. This study employs a deep learning-based model to develop a multilayer UNet architecture with an embedded attention mechanism, the AME-UNet, for high-resolution lightning nowcasting in North China. The AME-UNet model combines precise lightning-location data from the State Grid Corporation of China and “high spatiotemporal resolution” data from the FY-4A geostationary meteorological satellite, thereby creating a robust, multisource data foundation. Brightness temperature channel differences, which physically characterize cloud-top development heights and freezing levels, are employed as predictors for pixel-wise lightning nowcasting at 0–1 and 1–2 h lead times. The results demonstrate the competitive performance of AME-UNet, with probabilities of detection values of 0.46 (0–1 h) and 0.41 (1–2 h) while maintaining false alarm rates values of 0.29 and 0.45, respectively. This study presents novel deep learning approaches for lightning nowcasting, advancing the methodological toolkit for severe weather prediction.