A modular framework to run AI-based models from high-resolution climate projections

Recent advances in AI-based weather and climate models promise transformative improvements in forecasting, yet their integration with state-of-the-art climate simulations remains constrained by data heterogeneity. High-resolution projections, such as those from the Destination Earth Climate Digital Twin, are produced at 5 km resolution with specialized grids, formats, and variable sets that are incompatible with most AI models. Current integration efforts are ad-hoc, model-specific, and difficult to reproduce, slowing progress and limiting large-scale evaluation.Taking advantage of decades of experience in climate workflows, we propose a modular, source-agnostic framework that enables systematic and reproducible execution of AI-based climate models across diverse datasets and high-performance computing environments. The framework standardizes data preparation, automates model execution through containerized workflows, and provides built-in post-processing and evaluation tools. Preliminary experiments show that the framework reproduces and extends recent studies on AI model robustness in future climates with minimal technical overhead.