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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.

Particulate matter (PM2.5 and PM10) and ozone (O3) are the two major air pollutants in China in recent years. The fluctuations of PM2.5, PM10 and O3 strongly depend on the weather processes and anthropogenic emission. These processes may lead to the existence of short- and long-term memory behaviors in air pollutants. Hence, here we use the autoregressive parameter a of the first-order autoregressive process [AR (1)] to characterize the short-term memory effects of pollutants. We estimate the scaling exponent α using detrended fluctuation analysis (DFA) for the long-term memory effects of air pollutants (PM2.5, PM10, and O3) in summer and winter for different cities in China. Our results show that PM2.5, PM10, and O3 have strong short-term and long-term memory characteristics both in summer and winter. Furthermore, both the short- and long-term memory effects are stronger in winter than summer for most cities associated with stronger and longer persistent weather systems in winter. In general, the scaling exponent α of PM2.5 and PM10 are smaller for northern cities than those of southern cities in China. The long-term memory patterns of O3 are stronger in northern cities and weaker in southern cities in relative to those of PM2.5 and PM10 in winter. Our results show that the short- and long-term memory behaviors of air pollutions are dominated by the weather systems with different time scales.

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 global reanalysis of near-surface daily air temperature data, spanning 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 the network modularity, the number of communities, and the average community size over the past 30 years. Notably, the community structure of the climate network undergoes a discernible transition around 1982. We attribute this transition to the substantial increase in isolated nodes after 1982, 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 propose that the mechanism behind the amplified isolation in the climate network can be understood through weakened ocean current interactions under global warming. [ABSTRACT FROM AUTHOR]

Extreme drought events have frequently occurred in Southwest China (SWC) in this century. The rainfall of SWC could be related to several climate systems such as the East Asia monsoon, Indian monsoon and El Nino-southern Oscillation. Also it tightly depends on the variety of landforms and the complex terrain of SWC. Therefore the mechanism and prediction of rainfall in the area have became a difficult and central issue in climatology. Here we develop a novel multi-variable network method to delineate the relation between the global sea surface temperature anomalies (SSTA) and the rainfall of SWC. Our results show the important degree patterns in the Western, Eastern Pacific Ocean and Indian Ocean, which significantly influence the rainfall in SWC. Particularly the patterns will change with season and connect to some specific subareas within SWC. The strongest teleconnection is observed for the spring rainfall. The underlying mechanism of our observed teleconnection could be related to the large-scale ocean–atmosphere circulations. Moreover, we can identify the time-lag of the teleconnection links that can potentially improve the prediction of rainfall in SWC. [ABSTRACT FROM AUTHOR]