Deep‐learning weather prediction models have demonstrated skill in simulating large‐scale climate modes, but their physical interpretability remains a critical challenge. Here, we apply a 360‐member Green's function‐like ensemble to the Pangu‐Weather model to diagnose the optimal forcing configuration of the tropical western North Pacific anomalous anticyclone. The model reproduces the observed optimal forcing structure, including cooling over the western North Pacific and warming over the tropical Indian Ocean, demonstrating its ability to simulate physically consistent large‐scale responses. However, the model exhibits systematic biases, including a spurious westerly anomaly east of Indian Ocean heating and underestimated response to tropical Atlantic heating. These findings highlight both the physical consistency of Pangu‐Weather in capturing key climate modes and the challenges arising from its data‐driven nature. Our results underscore the need for hybrid modeling approaches that combine data‐driven learning with physical constraints to improve climate predictability and interpretability.