The Tibetan Plateau,often referred to as the “Roof of the World” and the “Third Pole”,is of considerable importance due to its high altitude,vast scale,and complex terrain,rendering it a pivotal element in global climate dynamics.In the last five decades,the plateau has witnessed a pronounced warming trend,with temperatures increasing at a rate twice that of the global average.Precise forecasting of future climate change in this region is paramount for various sectors,including agriculture,ecosystems,and socio-economic development.This study employs data from an experiment involving 18 models in the CMIP6 model,wherein the CO₂ concentration suddenly quadruples (abrupt-4×CO₂),to investigate the response of the Tibetan Plateau to greenhouse gas forcing.Specifically,the study focuses on feedback processes using the climate feedback response analysis method (CFRAM).The findings reveal that surface warming on the plateau is primarily driven by greenhouse gas forcing and positive water vapor feedback,further amplified by albedo and cloud feedback.Processes such as surface heat storage,sensible heat,and latent heat play roles in moderating temperature increases.Cloud feedback emerges as a significant source of uncertainty in plateau warming response,followed by albedo and water vapor feedbacks,while sensible and latent heat processes aid in mitigating this uncertainty.Variations in projected warming,particularly in central-eastern and southern regions of the plateau,stem from inter-model differences in surface heat storage and atmospheric dynamics.Enhanced parameterization to surface albedo and cloud cover is identified as an effective strategy to alleviate spatial uncertainty in model predictions of regional warming across the Tibetan Plateau.The spatial distribution of uncertainty in feedback processes varies,with maximum standard deviations observed in different regions for each process,corresponding to areas projected to experience significant warming.In summary,although greenhouse gas forcing models generally exhibit consistent trends across the Tibetan Plateau,variations in feedback processes and regional dynamics highlight the necessity for enhanced parameterization and resolution in climate models to improve predictions in this pivotal region.