This study presents a comprehensive global evaluation of climate model cloud biases using COSP instrument simulators and their corresponding satellite observations. The principle finding is that COSP-enabled comparisons robustly show that the physics parameterizations in CAM5 have dramatically reduced three long-standing climate model global cloud biases: (1) the underestimation of total cloud (2) the overestimation of optically thick cloud (3) the underestimation of midlevel cloud. The CAM5 midlevel cloud results suggest that climate models underestimate midlevel cloud fraction when the impact of snow on radiative transfer in the atmosphere is neglected. In contrast, CAM4 has large compensating biases in cloud optical properties and cloud amount. The most striking regional improvements in cloud representation from CAM4 to CAM5 include decreased optically thick high-topped cloud in the deep convective tropical Pacific, increased midlatitude storm track cloud, increased low cloud in the stratocumulus regimes, and an improved seasonal cycle of Arctic clouds. Although CAM5 is a closer match to observations, both CAM versions underestimate total cloud when compared to observations. Of particular importance for climate simulations are significant biases in subtropical marine boundary layer clouds. While CAM5 is a clear improvement over CAM4, neither model correctly captures the stratocumulus-to-cumulus transition. The use of COSP-enabled comparisons was critical for documenting these improvements because, despite having dramatically different cloud fractions and cloud optical properties, compensating errors often allow CAM4 and CAM5 to have similar biases in cloud radiative effect.