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 handle 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 nested 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存在系统性偏差。现有的多变量偏差校正方法无法显式处理时空依赖性。然而，保持时空一致性和变量间一致性对于真实的气候动力学和可靠的区域影响评估至关重要。针对这一空白，我们提出名为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解耦时空确定性效应与随机残差。为建模变量间和位置间的联合分布及依赖性，我们引入嵌套藤蔓连接函数，这是一种分层藤蔓合并策略。在多变量偏差校正背景下，嵌套藤蔓连接函数结合了两个依赖层级：(i) 各变量分别建模的空间位置依赖性，(ii) 在选定参考位置建模的变量间依赖性——该层级将空间模型整合为连贯的多变量空间结构。瑞士实例应用表明，该方法在多种评估指标上均能更好保持变量间、空间和时间的依赖性。