Abstract: Earth system models are essential for understanding and predicting complex climate dynamics, yet they often exhibit persistent biases and may reproduce observed behavior for the wrong dynamical reasons. In this talk, I will present two AI-enabled approaches for improving and interrogating such models. First, I will introduce an explainable AI framework for bridging idealized and comprehensive Earth system models. Even highly coarse-grained models that represent only a small subset of variables can accurately capture selected dynamics and statistics. Through physically augmented latent representations and latent data assimilation, such targeted information can be propagated into the full high-dimensional Earth system state, improving variables and regions not directly represented by the idealized model. The resulting computationally efficient framework enables longer, high-resolution, and multivariate datasets for studying extremes, uncertainty, sensitivity, and related emulation and digital-twin applications. Second, I will present a reconstruction-based framework that treats observations as active probes of model dynamics. The method identifies information pathways that the model itself relies on and challenges these same pathways with observational information. Reconstruction fingerprints can expose and localize pathway-specific deficiencies at the mechanistic level, even when conventional model outputs appear realistic. These diagnoses provide systematic guidance for model improvement and help establish the mechanistic credibility needed for trustworthy predictions under changing environmental conditions. Both frameworks are demonstrated using CMIP6 Earth system models, with ENSO serving as a common testbed for model improvement and mechanism-level diagnosis.