Abstract. Full-scale Earth System Models (ESMs) are too computationally expensive to keep pace with the growing demand for climate projections across a large range of emissions pathways. Climate emulators, reduced-order models that reproduce the output of full-scale models, are poised to fill this niche. However, the large number of emulation techniques available and lack of a comprehensive theoretical basis to understand their relative strengths and weaknesses compromise fundamental methodological comparisons. Here, we present a theoretical framework that connects disparate emulation techniques and use it to understand potential sources of emulator error focusing on memory effects, hidden variables, system noise, and nonlinearities. This framework includes popular emulation techniques such as pattern scaling and response functions, relating them to less commonly used methods, such as Dynamic Mode Decomposition and the Fluctuation Dissipation Theorem (FDT). To support our theoretical contributions, we provide practical implementation guidance for each technique. Using pedagogical examples including idealized box models and a modified Lorenz 63 model, we illustrate the expected errors from each emulation technique considered. We find that response function-based emulators outperform other techniques, particularly pattern scaling, across all scenarios tested. Potential benefits and trade-offs from incorporating statistical mechanics in climate emulation through the use of the FDT are discussed, along with the importance of designing future scenarios for ESMs with emulation in mind. We argue that large-ensemble experiments utilizing the FDT could benefit climate modeling and impacts communities. We conclude by discussing optimal use cases for each emulator, along with implications for ESMs based on our pedagogical model results.
Womack et al. (2026) studied this question.