The Tibetan Plateau (TP) is known as one of the most sensitive regions to climate change, and it has experienced accelerated warming in recent decades. However, to what degree the TP warming amplification relates to remote forcing such as sea ice loss in the Arctic sea ice remains unclear. Here, we found that the decline of sea ice concentration over the Barents-Kara Sea (BKS) could account for 18–32% of the winter warming over the TP by comparing observational data and ensemble experiments from an atmospheric general circulation model. The reduced BKS sea ice and resultant upward turbulent heat fluxes can intensify a Rossby wave train propagating equatorward to the TP. As a result, the enhanced southwesterlies towards the TP strengthen the warm advection over most parts of the TP and lead to TP warming. In addition, an atmospheric teleconnection between the Arctic and the TP also exists in the interannual variability. That is, a tripole mode in air temperature, with warm centers in the Arctic and TP but a cold center in the mid-high latitudes of the Eurasian continent in between. Our results imply that the BKS sea ice loss could intensify such a tripole mode and thus enhancing the winter TP warming.

Accurate prediction of the East Asian summer monsoon (EASM) is beneficial to billions of people’s production and lives. Here, a convolutional neural network (CNN) and transfer learning are used to predict the EASM. The results of the constructed CNN regression model show that the prediction of the CNN regression model is highly consistent with the reanalysis dataset, with a correlation coefficient of 0.78, which is higher than that of each of the current state-of-the-art dynamic models. The heat map method indicates that the robust precursor signals in the CNN regression model agree well with previous theoretical studies and can provide the quantitative contribution of different signals for EASM prediction. The CNN regression model can predict the EASM one year ahead with a confidence level above 95%. The above method can not only improve the prediction of the EASM but also help to identify the involved physical predictors.

The thermal condition over the Tibetan Plateau (TP) plays a vital role in the South Asian high (SAH) and the Asian summer monsoon (ASM); however, its prediction skill is still low. Here, two machine learning models are employed to address this problem. Expert knowledge and distance correlation are used to select the predictors from observational datasets. Both linear and nonlinear relationships are considered between the predictors and predictands. The predictors are utilized for training the machine learning models. The prediction skills of the machine learning models are higher than those of two state-of-the-art dynamic operational models and can explain 67% of the variance in the observations. Moreover, the SHapley Additive exPlanation method results indicate that the important predictors are mainly from the Southern Hemisphere, Eurasia, and western Pacific, and most show nonlinear relationships with the predictands. Our results can be applied to find potential climate teleconnections and improve the prediction of other climate signals.

The surface heating on the Tibetan Plateau (TP) exerts large influence on the Asian summer monsoon. Drivers of TP surface heat include the tropical forcings and other climate modes in middle and high latitude of the Northern Hemisphere, however whether the climate modes in Southern Hemisphere influence the TP surface heat is rarely studied. Using multiple source data diagnosis and numerical experiment from an atmospheric general circulation model (AGCM), we found that the Antarctic Oscillation (AAO) in May can efficiently modulate the subsequent surface heat source over the TP in June. When the AAO is in positive phases, a northeastward propagating atmospheric Rossby wave train originates from the Amundsen Sea low. As a part of this wave train, a pair of anomalous cyclone and anticyclone in the Southern Hemisphere accelerates the surface southeasterlies between them, accompanied by cold sea surface temperature (SST) anomaly in the equatorial middle and eastern Indian Ocean induced by the wind‐evaporation‐SST feedback. Due to the large thermal inertia, in June, the cold SST anomaly stimulates anticyclone anomalies over the western TP and eastern Arabian Sea, which increase the moisture transportation toward the TP and are conducive to the formation and maintenance of the precipitation over the middle and eastern TP. In contrast, the surface heat source, which is dominated by the upward sensible heat flux, is reduced substantially. This result indicates that the AAO can be one of the precursors of the TP heat source, and may help improve the prediction of the Asian summer monsoon.

Most global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate a delayed onset of the tropical Asian summer monsoon of 3–6 pentads when compared with the observations. However, a clear explanation of this model bias has yet to be developed. This study indicates that 23 of the 31 of CMIP6 models generate both the Tibetan Plateau (TP) cold bias and the delayed monsoon onset across tropical Asia. The aloft TP cold air temperature associated with these models tends to reduce the land–sea thermal contrast and monsoon circulation, and hence it generates a delayed onset for the tropical summer monsoon. Two sensitivity experiments based on a coupled ocean–atmosphere–land GCM, together with additional data analysis, further confirm the underlying connection between monsoon onset and temperature anomaly over the TP. Therefore, it is of great importance that we attempt to reduce the model bias associated with the simulation of monsoon onset by improving the physical process parameterization scheme related to the TP temperatures.