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.

The Tibetan Plateau (TP) is known as one of the sentinels of global climate change. Substantial winter warming over the TP will likely lead, directly or indirectly, to a series of geological disasters such as snow and glacial avalanches. Hence, for better adaptation to climate change, it is vital to project the future change in winter temperature over the TP. However, the current state-of-the-art climate models involved in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) still produce strong cold biases over most parts of the TP in their historical simulations. On the basis of selecting the optimal models, here we use the statistical downscaling method to constrain the projected winter temperature in CMIP6 models. The results show that the regions with the strongest winter warming over the TP will be near the Himalayas and the densely populated eastern regions. The constrained warming magnitude is much greater than that in the ensemble mean of the original 32 CMIP6 models or six best models over these regions. Therefore, early warning and forecasting services should be strengthened for the future temperature over these regions. Moreover, the long-term spatial warming varies greatly under four different future emission scenarios. Under the most severe scenario, the increase in winter temperature near the Himalayas exceeds 10℃, which will greatly destabilize glaciers in the region, while the increase is only 4–6℃ under the weakest scenario. Therefore, it is urgent to reduce greenhouse gas emissions to control the future temperature increase at hotspots of climate vulnerability such as the TP.