Cloud feedbacks are the leading cause of uncertainty in climate sensitivity. The complex coupling between clouds and the large‐scale circulation in the tropics contributes to this uncertainty. To address this problem, the coupling between clouds and circulation in the latest generation of climate models is compared to observations. Significant biases are identified in the models. The implications of these biases are assessed by combining observations of the present day with future changes predicted by models to calculate observationally constrained feedbacks. For the dynamic cloud feedback (i.e., due to changes in circulation), the observationally constrained values are consistently larger than the model‐only values. This is due to models failing to capture a nonlinear minimum in cloud brightness for weakly descending regimes. Consequently, while the models consistently predict that these regimes increase in frequency in association with a weakening tropical circulation, they underestimate the positive cloud feedback associated with this increase. Plain Language Summary Climate models are crucial for understanding the impacts of climate change. Yet there are significant differences between models even for the global mean temperature increase due to a doubling of carbon dioxide concentrations (known as the climate sensitivity). These differences in climate sensitivity are largely due to differences in cloud responses to warming between the models (known as cloud feedbacks). Cloud feedback uncertainty is particularly large for tropical clouds. One reason for this is the two‐way interactions between clouds and the large‐scale circulation. This remains relatively poorly understood and involves processes that occur at a wide range of scales which cannot be captured by climate models. This study examines relationships between clouds and circulation in the tropics in the latest generation of climate models. Cloud feedbacks can be separated into dynamic (changes in circulation) and thermodynamic (changes in cloud for a given circulation regime) components. We use this framework to calculate the cloud feedbacks models would predict if the relationships between clouds and circulation matched observations. This results in an increase in the dynamic component of the cloud feedback which is due to the models' failure to capture the observed nonlinear relationships between clouds and circulation. Key Points Climate models miss key details of the observed links between tropical clouds and circulation, notably for common weak subsidence regimes Using observations to constrain thermodynamic and dynamic cloud feedbacks in models increases the dynamic and hence total feedback This is due to subtle nonlinear changes in the observed SW cloud radiative effect across circulation regimes that the models predict will change in frequency