Based on the complex network method, this study reveals the temporal and spatial characteristics of the synchronization of extreme rainfall events in southwest China (SWC) during the main rainfall season (May–September). Our results show the significant synchronization pattern of extreme rainfall events in SWC, which is closely associated with the convergence of warm‐wet air and cold‐dry air and the corresponding occurrence of extreme rainfall in SWC. The network divergence indicates a dominated direction of the net influence of extreme rainfall from the Sichuan Basin to the Yunnan–Guizhou Plateau, implying the extreme rainfall synchronization moving from north to south in SWC is more frequent than the opposite direction. The development of the low vortex and the north wind system usually shows a movement from north to south, resulting in the evolution of extreme rainfall events more tend to present a southward movement. The low‐reinforced latitude cyclonic circulation leads to the strengthened transportation of water vapour from the tropical area to the north and converges with the anticyclonic circulation in the mid‐latitudes to form the movement of heavy rainfall events from the Yunnan–Guizhou Plateau to the Sichuan Basin. Therefore, the synchronization direction of extreme rainfall events in SWC has obvious asymmetry spatial feature due to the more frequent and stronger circulation system moving from north to south than the opposite.

Droughts and floods have frequently occurred in Southwest China (SWC) during the past several decades. Yet, the understanding of the mechanism of precipitation in SWC is still a challenge, since the East Asian monsoon and Indian monsoon potentially influence the rainfall in this region. Thus, the prediction of precipitation in SWC has become a difficult and critical topic in climatology. We develop a novel multi-variable network-based method to delineate the relations between the global sea surface temperature anomalies (SSTA) and the precipitation anomalies (PA) in SWC. Our results show that the out-degree patterns in the Pacific, Atlantic and Indian Ocean significantly influence the PA in SWC. In particular, we find that such patterns dominated by extreme precipitation change with the seasons. Furthermore, we uncover that the teleconnections between the global SSTA and rainfall can be described by the in-degree patterns, which dominated by several vital nodes within SWC. Based on the characteristics of these nodes, we find that the key SSTA areas affect the pattern of the nodes in SWC with some specific time delays that could be helpful to improve the long-term prediction of precipitation in SWC.

Our study utilizes a global reanalysis of near-surface daily air temperature data spanning the years from 1949 to 2019 to construct climate networks. By employing community detection for each year, we reveal the evolving community structure of the climate network within the context of global warming. Our findings indicate significant changes in measures such as network modularity and the number of communities over the past 30 years. Notably, the community structure of the climate network has undergone a discernible transition since the early 1980s. We attribute this transition to the substantial increase in isolated nodes since the 1980s, primarily concentrated in equatorial ocean regions. Additionally, we demonstrate that nodes experiencing amplified isolation tend to diminish connectivity with other nodes globally, particularly those within the same oceanic basin, while showing a significant strengthening of connections with the Eurasian and North African continents. We deduce that the mechanism driving amplified isolation in the climate network may be comprehended through the weakening of tropical circulations, such as the Hadley cell and Walker circulation, in response to increasing greenhouse gases.

By constructing a correlation network between global sea surface temperature anomalies (SSTAs) and summer precipitation anomalies in the Yunnan–Guizhou Plateau, key SST regions influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau were selected. It was found that spring SSTAs in the Bay of Bengal, southwestern Atlantic, and eastern Pacific are crucial for influencing summer precipitation anomalies in the Yunnan–Guizhou Plateau. Setting SSTAs from these three regions as predictor variables 3 months in advance, we constructed multiple linear regression (MLR), ridge regression (RR), and lasso regression (LR) models to predict summer precipitation anomalies over the Yunnan–Guizhou region. The training phase involved data spanning from 1961 to 2005, which aimed to predict precipitation anomalies in the Yunnan–Guizhou Plateau for the period extending from 2006 to 2022. Based on MLR, RR, and LR models, the correlations between predicted values and observed summer precipitation anomalies in Yunnan–Guizhou were 0.48, 0.46, and 0.46, respectively. These values were all higher than the correlation coefficients of the NCC_CSM model’s predicted and observed values. Additionally, its performance in predicting summer precipitation anomalies over the Yunnan–Guizhou region, based on key SST regions, was assessed using performance metrics such as anomaly correlation coefficient (ACC), anomaly sign consistency rate (PC), and trend anomaly comprehensive score (PS score). The average ACC of MLR, RR, and LR models was higher than that of the NCC_CSM model’s predictions. For MLR, RR, LR, and NCC_CSM models, the PCs exceeding 50% of the year were 14, 14, 11, and 10, respectively. Furthermore, the average PS score for predicting summer precipitation anomalies over the Yunnan–Guizhou region using MLR, RR, and LR was approximately 73 points; 8 higher than the average PS score of the NCC_CSM model. Therefore, predicting summer precipitation anomalies over the Yunnan–Guizhou region based on key SST regions is of great significance for improving the prediction skills of precipitation anomalies in this region.

This study utilizes data from 1979 to 2023 and employs a 1-year sliding window approach to construct networks for each 10-year period, analyzing the synchronization patterns of extreme rainfall events in Southwest China. The average weighted degree of these networks is defined as a synchronization index, which quantifies the synchronization between rainfall events across the region. The results reveal a significant upward trend in the synchronization index, indicating that extreme rainfall events occurring simultaneously in Southwest China are steadily increasing. Furthermore, through partial correlation, contribution rate, and composite analyses, the study demonstrates that the Indian Ocean Warm Pool Strength Index (IOWPSI) independently influences the synchronization patterns of extreme rainfall events in Southwest China. In contrast, the Western Pacific Warm Pool Strength Index (WPWPSI) exerts a comparatively weaker independent effect on synchronization in this region. However, the interaction between the IOWPSI and the WPWPSI modulates the Western Pacific Subtropical High, thereby enhancing its influence on the synchronization of extreme rainfall events in Southwest China.