Atmospheric bores are a type of boundary-layer internal gravity wave with a widespread global distribution. They play a significant role in the initiation and organization of convective activities, particularly nocturnal convection, and have received considerable attention from the international research and operational forecasting communities. However, they have garnered relatively little focus in China. To enhance the awareness and understanding of atmospheric bores among domestic researchers and forecasters, this paper systematically reviews recent research progress in this field within China. Building upon a review of relevant international studies, this work summarizes the primary characteristics of atmospheric bores over the Yangtze-Huai Plain. These include typical manifestations such as relatively weak radar echo intensity and the challenge of distinguishing them from the background environment, as well as climatological statistical patterns including concentrated distributions in hotspot regions and outbreaks under specific synoptic conditions. Furthermore, through the analysis of multiple typical cases, this paper elaborates in detail on the generation mechanisms and evolutionary features of atmospheric bores in China under interactions between different types of convective outflow boundaries (such as cold fronts, cold pools, gust fronts, and sea-breeze fronts) and their coupling with the boundary layer. It also analyzes the multifaceted impacts of bores on the initiation and development of convection under various environmental conditions, while revealing the specific role of this typical nocturnal phenomenon in the evolution of daytime severe convection. Finally, the paper concludes that there is an urgent need to deepen the understanding of the complex coupling and feedback mechanisms between atmospheric bores and convection in China; to enhance the improvement and applicability of current model parameterization schemes; and to develop precise identification and forecasting techniques for atmospheric bores and their associated convection by integrating comprehensive observational datasets with machine learning algorithms.