Abstract: This article reviews advances in monitoring and nowcasting of severe convective weather (SCW), along with developments in operational nowcasting systems. It focuses on deep learning (DL)-based techniques using multisource data, highlighting associated challenges and opportunities. Based on multisource observations including those from dual-polarization weather radars and geostationary satellites, the monitoring capabilities of SCW types and intensities, convective initiation, and identification and tracking of convective storm cells have been significantly improved using advanced technologies, including storm structural feature recognition, fuzzy logic, and DL. Among these approaches, deep generative models have proven particularly effective, substantially improving the accuracy and extending the lead time of SCW nowcasting. The performance of the China Meteorological Administration's Severe Weather Analysis and Forecasting (SWAN) 3.0 system continues to advance, with widespread operational adoption across China. Future efforts will leverage higher-resolution observations and numerical weather prediction products at the hundred-meter resolution to enhance the understanding of the underlying mechanisms of SCW development at meso-γ- and microscales. Current purely data-driven AI models are transitioning toward physics-informed frameworks for SCW nowcasting. Integrating forecasters' operational expertise with state-of-the-art AI technology will further enhance operational capabilities in monitoring and nowcasting extreme SCW events.