A first-principles data-modelling approach applied to multi-scale planetary fluid loops, focusing on the Atlantic Meridional Overturning Circulation (AMOC) conveyor and the Equatorial Pacific El Niño–Southern Oscillation (ENSO) heat engine. This paper outlines a scale-invariant thermodynamic protocol that enforces a rigid temporal grid to map continuous macro-environmental telemetry. By tracking sub-grid multiscale variability and high-frequency vorticity fields, the protocol identifies localized thermodynamic transitions prior to macroscopic structural failure. The framework bridges pure fluid dynamics with generative machine learning architectures, providing a computationally efficient method to optimise ensemble climate simulations and resolve fine-scale uncertainty bounds without heavy infrastructure overhead.