GCD: GCM-consistent Diffusion for Zero-shot Downscaling across Heterogeneous GCMs

Climate downscaling of global General Circulation Models (GCMs) is a challenging ill-posed inverse problem. In practice, the lack of paired data makes end-to-end supervised training infeasible, while downstream assessments require processing large, heterogeneous GCM ensembles under strict computational limits. Consequently, developing an efficient, zero-shot downscaling approach is crucial. While vanilla Diffusion Posterior Sampling has recently shown immense potential by bypassing paired training, it remains bottlenecked by geographic misalignments, spectral biases, and prohibitive inference costs. To address these limitations, we propose the GCM-consistent Diffusion for Zero-shot Downscaling (GCD) framework. First, GCD conditions the diffusion prior on static geographic boundaries to ensure physical fidelity. Second, we introduce a filter measurement operator, effectively mitigating spectral discrepancies between GCMs and the prior to provide stable inference-time guidance. Third, to overcome the inference bottleneck, we design a joint acceleration scheme: by combining progressive distillation with Conjugate Gradient guidance to correct large-step deviations, GCD compresses the generation process to just 6 steps. Extensive experiments demonstrate that GCD achieves highly competitive 99th percentile accuracy across five heterogeneous GCMs and successfully recovers the high-frequency details of tropical cyclones, entirely without model-specific retraining.