Physics-based climate projections using general circulation models are essential for assessing future risks, but their coarse resolution limits regional decision-making. Statistical downscaling can efficiently add detail, yet many methods treat variables independently, degrading inter-variable relationships that govern compound hazards such as heat stress, drought, and wildfire. Here we show that a diffusion-based multivariate generative framework, combined with bias correction, recovers degraded inter-variable correlations even under a 50$\times$ increase in linear resolution. When applied to five meteorological variables over Japan, the framework reduces inter-variable correlation errors by more than fourfold relative to existing baselines while improving both univariate and spatial accuracy, leading to more accurate detection of severe drought. These results demonstrate that multivariate generative downscaling improves the reliability of compound risk assessment under large resolution gaps.

翻译：基于通用环流模型的物理驱动气候预测对于评估未来风险至关重要，但其粗分辨率限制了区域决策制定。统计降尺度可有效补充细节信息，然而许多方法将变量独立处理，破坏了控制热应激、干旱和野火等复合灾害的变量间关系。本文证明，结合偏差校正的扩散式多变量生成框架，即使在50倍线性分辨率提升下仍能恢复退化的变量间相关性。将该框架应用于日本地区五个气象变量时，相比现有基线方法，变量间相关性误差降低四倍以上，同时提升单变量精度和空间精度，从而更准确地检测严重干旱。这些结果表明，多变量生成式降尺度可提升大分辨率差距下复合风险评估的可靠性。