How can climate model emulators be aligned more closely with the needs of applied researchers?

Earth System Models (ESMs) represent our most comprehensive tools for understanding and projecting climate change impacts; yet, they are highly computationally demanding and technically complex. Climate model emulators offer an alternative approach by approximating components or full ESM outputs at a reduced computational cost. Such emulators can range from reduced-order climate models to fully data-driven machine learning surrogates. As the demand for climate information increases, interest in climate model emulation has grown across both climate science and machine learning research, leading to rapid methodological development. Despite this shared interest, the two research fields remain largely disconnected and the application of machine learning climate emulators in climate science remains challenging [1]. Many emulators, therefore, remain unused in decision-making contexts--not because they lack value, but because methodological developers and users lack a shared framework for communication, evaluation, and practical guidance. 
This work examines this disconnect and takes a step towards facilitating the use of machine learning–based climate emulators in applied research and decision-making. We analyze and contrast methodological and applied perspectives on emulators, identify points of misalignment, and highlight opportunities for improved interaction. Building on these insights, we propose a tutorial-style framework that connects the two perspectives and provides practical guidance for developing, evaluating, and using climate emulators in research and decision-making contexts.

[1] Fowler, H. J., Mearns, L. O. and Wilby, R. L. [2025], Downscaling future climate projections: Compound-
ing uncertainty but adding value?, in ‘Uncertainty in Climate Change Research: An Integrated Approach’,
Springer, pp. 185–197.