Abstract: The first generation of machine learning weather emulators performed well on large scale weather patterns but still struggle with smaller scale phenoma like convection and correctly representing connections across variables and parts of the Earth system. Further significant improvements in prediction accuracy and physical realism will require more than just additional data and larger models. A new foundation for how we conceptualize and organize AI Earth system prediction pipelines is needed. CREDIT (Community Research Earth Digital Intelligence Twin) Generation 2 aims to be that new foundation with a significant refactoring to enable composable components for pre-processing, modeling, and post-processing atmosphere, ocean, chemistry, and other Earth system component models. CREDIT can now ingest a broader range of datasets, and differentiable pipelines enable unpacking at every stage to understand how data flow through time to drive better understanding and prediction accuracy. Examples of CREDIT in action and analysis of CREDIT components will be presented.