Reconstructing past climate variability requires computationally efficient models able to capture the behaviour of complex climate systems. We develop a suite of climate model emulators that improve the representation, reconstruction, and prediction of spatial climate variability compared to traditional approaches. Results highlight the importance of predictability-oriented representations and nonlinear dynamical memory for scalable emulators suited to paleoclimate data assimilation frameworks.