Emulating absolute annual temperatures and variability from the CESM2 Large Ensemble using a diffusion model

Exploring uncertainty and internal variability across future emission pathways remains computationally demanding with state-of-the-art Earth system models (ESMs). We present a diffusion-based machine-learning emulator trained on output from the CESM2 large ensemble dataset to reproduce absolute annual-mean temperature and year to year variability,  conditioned on anthropogenic co2 and sulfate emisisson from ssp3-7.0 scenario. The emulator employs a three-dimensional UNet architecture that learns the spatiotemporal distribution of global temperature fields in latitude–longitude–time space. Conditioning variables include cumulative CO₂ and aerosol emissions, enabling the generation of physically consistent climate responses under arbitrary emission trajectories.To enhance physical interpretability, we integrate explainable AI (XAI) methods, including gradient-based attribution and sensitivity analyses, to quantify how emission-related conditioning variables influence regional temperature responses. The emulator reduces computational cost by several orders of magnitude compared to full ESM simulations, enabling rapid scenario exploration and uncertainty assessment. This framework aims provides a scalable and interpretable pathway for fast climate response emulation