Additional review

Abstract. Mesoscale Convective Systems (MCSs) are critical components of the climate system and are frequently responsible for extreme precipitation and other catastrophic weather events. Rapid and accurate identification of MCSs can significantly enhance our ability to respond to such extreme events. Traditionally, MCSs identification has relied on threshold-based methods, which are often limited by slower processing speeds and smaller detection areas. Recent advancements in deep learning techniques for object recognition offer a promising alternative for MCSs identification. In this study, we propose an advanced approach to address the challenges associated with traditional threshold-based MCSs identification by creating a specialized dataset and training an MCSs recognition model. First, we constructed an MCSs identification dataset based on infrared satellite data, covering a spatial range (60° S – 60° N, 180° W – 180° E), and a temporal range from 2011 to 2023. Subsequently, by integrating a significance learning strategy and a multi-scale feature extraction method, we developed MCSeg, a novel MCSs recognition model tailored specifically for mid- and low-latitude regions. Finally, we compared the MCSs identified using MCSeg with those identified using the threshold method and conducted precipitation event analysis. The results of the two methods showed a high degree of consistency, indicating the feasibility of applying deep learning methods to MCSs identification.