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This study estimates short-term climate feedbacks by using a new set of radiative kernels applied to observations and the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The new kernels are generated based on multiyear satellite observations, and they can well reproduce the top-of-atmosphere (TOA) radiation budget. The choice of radiative kernels influences the feedback estimation, especially the surface albedo feedback and cloud feedback in the Arctic and the Southern Ocean. Observational estimates show that tropospheric water vapor feedback makes the largest contribution to global warming, while lapse rate feedback is the largest contributor to local warming over East Asia. Compared to the observations, biases occur but differ when simulating global and East Asian local climate feedbacks. CMIP6 models overestimate global mean Planck, lapse rate, stratospheric temperature and water vapor, and cloud feedbacks, but underestimate global mean tropospheric water vapor and surface albedo feedbacks. Over East Asia, local Planck and lapse rate feedbacks are underestimated, while tropospheric water vapor, stratospheric temperature, and cloud feedbacks are overestimated. The simulation biases in local longwave (LW) and shortwave (SW) cloud feedbacks over East Asia are considerable, probably due to the failure in simulating cloud fraction response of marine cirrostratus, deep convective cloud, and stratus. The intermodel spread of cloud feedback is the largest for both global and East Asian local feedback processes. Our results suggest that contemporary climate models are still difficult to accurately simulate global and local climate feedback processes. [ABSTRACT FROM AUTHOR]