Interactive Climate Projection via Conditional Generative AI

Abstract Dynamical Earth System Models are extremely expensive, requiring ∼10 6 core‐hours for century‐scale simulations. This limits climate projections to only a few Shared Socioeconomic Pathways (SSPs) and leaves numerous policy trajectories unexplored. Here, we develop an external forcing boundary‐constrained generative emulator trained on CMIP6 model outputs, enabling rapid projection of temperature, precipitation and sea‐surface height under arbitrary CO 2 forcing pathways. The model learns the continuous manifold of future climate states by conditioning on the physical boundary defined by two extreme scenarios (SSP1‐1.9 and SSP5‐8.5). It can reconstruct climate responses under pathways held out during training (e.g., SSP2‐4.5). We further apply it to four newly constructed CO 2 pathways (higher‐emission, lower‐emission, overshoot, and step‐change) to demonstrate its generalization capability across distinct forcings. This allows us to capture regional shifts in extreme events and threshold timings. The emulator substantially reduces computational demand and provides a high‐throughput, user‐driven platform for evaluating flexible mitigation strategies.