Cloud feedback remains the dominant source of uncertainty in climate projections, highlighting the necessity of rigorous cloud-based evaluations of climate models. Current assessments rely predominantly on cloud climatology and responses to internal variability, leaving cloud changes driven by historical warming largely unassessed. Here, we identify an emergent trend mode in total cloud cover (CLT) across multiple reanalysis products that is closely linked to global mean surface temperature. Using this warming-linked mode as the primary benchmark, we evaluate 13 CMIP6 AMIP simulations (1979–2014). While the models adequately capture global warming and internal variability in both temperature and CLT, this CLT trend mode is systematically absent in the simulations. Diagnostic regression reveals that this absence is characterized by a substantial underestimation of the response amplitude and large-scale spatial mismatches. This systematic deficiency points to shared structural limitations in current atmospheric models. Addressing this specific discrepancy offers a targeted pathway to constrain the forced cloud response, thereby reducing cloud feedback uncertainties in future climate projections.