Climate models are essential for understanding large-scale climate dynamics and long-term climate change, yet they exhibit systematic biases when compared with historical observations. Existing multivariate bias correction (MBC) approaches do not explicitly handel spatiotemporal dependence. However, preserving both spatiotemporal and inter-variable consistency is essential for realistic climate dynamics and reliable regional impact assessments. To address this gap, we propose a novel MBC method called GN-VBC that uses generalized additive models (GAMs) to disentangle spatiotemporal deterministic effects from stochastic residuals. To model joint distributions and dependencies across variables and locations, we introduce nsted vine copulas (NVCs), a hierarchical vine merging strategy. NVC in the context of MBC combines two dependence levels: (i) spatial dependence across locations, modeled separately for each variable, and (ii) inter-variable dependence modeled at a selected reference location, which links the spatial models into a coherent multivariate and spatial structure. An application to Switzerland shows improvements in preserving inter-variable, spatial and temporal dependence across a wide range of evaluation metrics.

翻译：气候模型对于理解大尺度气候动力学和长期气候变化至关重要，但与历史观测数据相比，其存在系统性偏差。现有的多变量偏差校正方法未能显式处理时空依赖性。然而，保持时空一致性与变量间一致性对于实现真实的气候动力学和可靠区域影响评估具有关键意义。为填补这一空白，本研究提出一种名为GN-VBC的新型多变量偏差校正方法，该方法利用广义可加模型分离时空确定性效应与随机残差。为建模跨变量与跨位置的联合分布及依赖关系，我们引入了嵌套藤Copula——一种分层藤结构融合策略。在多变量偏差校正框架中，嵌套藤Copula整合了两个依赖层级：(i) 各变量分别建模的空间位置间依赖；(ii) 在选定参考位置建模的变量间依赖，该层级将空间模型联结为协调的多变量空间结构。在瑞士地区的应用案例表明，该方法在多种评估指标下均能有效提升对变量间、空间及时间依赖关系的保持能力。