A review of synchronization of extreme precipitation events in monsoons from complex network perspective

Monsoon precipitation is the critical source of freshwater for some of the world's most densely populated areas, yet extreme precipitation events in these regions present significant risks, including devastating floods and damage to agriculture and infrastructure. Recent events, such as the severe flooding and landslides during the 2023 North India monsoon and the 2022 Pakistan floods, ¹ underscore the pressing need to better understand and predict these hazards. While the science of monsoons has been studied for decades, with theories centered on global dynamics and moist energy budgets to explain the zonal mean state of monsoon and factors leading to regional differences, one key theme of all these analyses is the spatiotemporal variability of rainfall from the dry to wet seasons. A key challenge is understanding and predicting extreme rainfall incidents during monsoon seasons to help mitigate dire undesired consequences. In the past two decades, nonlinear system dynamics has emerged as a novel and promising approach in climate science research, with complex network analysis becoming one of the most rapidly developing methods. Complex networks offer a powerful tool to uncover interactions among various geographic locations and teleconnection patterns, providing new insights into the behavior of monsoon systems. Since its origin in graph theory, network dynamics has evolved to focus on metrics such as centrality and community detection, which when applied to monsoon precipitation, particularly extremes, reveal coherent structures that were previously unidentified. Notably, network communities have shown strong associations with the major monsoon regions, offering fresh perspectives on monsoon dynamics. This paper synthesizes recent studies on monsoon precipitation, particularly those employing network metrics to understand key physical processes. While both statistical and dynamical models continue to struggle with predicting extreme monsoon precipitation, complex network analysis has identified new predictors related to global monsoon teleconnection patterns. These predictors address non-stationarities caused by climate variability, presenting opportunities to enhance monsoon predictions. Nonlinear system science thus holds significant potential for deepening our understanding of the spatiotemporal variability of global monsoon and extreme weather events. Finally, this paper outlines a future research agenda aimed at addressing key knowledge gaps. These include expanding the regions of study to explore region-to-region teleconnections, enhancing the physical understanding of network metrics, applying coupled networks, investigating the interannual and interdecadal variability of monsoons, and utilizing network diagnostics of climate model evaluation.