Inter-variable correlations (e.g., between daily temperature and precipitation) are key statistical properties to characterise probabilities of simultaneous climate events and compound events. Their correct simulations from climate models, both in values and in changes over time, is then a prerequisite to investigate their future changes and associated impacts. Therefore, this study first evaluates the capabilities of one 11-single run multi-model ensemble (CMIP6) and one 40-member single model initial-condition large ensemble (CESM) over Europe to reproduce the characteristics of a reanalysis dataset (ERA5) in terms of temperature–precipitation correlations and their historical changes. Next, the ensembles’ correlations for the end of the 21st century are compared. Over the historical period, both CMIP6 and CESM ensembles have season-dependent and spatially structured biases. Moreover, the inter-variable correlations from both ensembles mostly appear stationary. Thus, although reanalysis displays significant correlation changes, none of the ensembles can reproduce them, with internal variability representing only 30% on the inter-model variability. However, future correlations show significant changes over large spatial patterns. Yet, those patterns are rather different for CMIP6 and CESM, reflecting a large uncertainty in changes. In addition, for historical and future projections, an analysis conditional on atmospheric circulation regimes is performed. The conditional correlations given the regimes are found to be the main contributor to the biases in correlation over the historical period, and to the past and future changes of correlation. These results highlight the importance of the large-scale circulation regimes and the need to understand their physical relationships with local-scale phenomena associated to specific inter-variable correlations.

变量间相关性（例如，每日温度和降水之间）是表征同时气候事件和复合事件概率的关键统计特性。他们对气候模型的正确模拟，无论是价值还是随时间的变化，都是调查其未来变化和相关影响的先决条件。因此，本研究首先评估了欧洲的一个 11 个单次运行的多模式集合 (CMIP6) 和一个 40 个成员的单模式初始条件大集合 (CESM) 的能力，以重现欧洲再分析数据集 (ERA5) 的特征。温度-降水相关性及其历史变化。接下来，比较了 21 世纪末集合的相关性。在整个历史时期，CMIP6 和 CESM 合奏都具有季节依赖性和空间结构偏差。此外，来自两个集合的变量间相关性大多是平稳的。因此，尽管再分析显示出显着的相关性变化，但没有一个集合可以重现它们，内部变异性仅代表模型间变异性的 30%。然而，未来的相关性显示出大空间模式的显着变化。然而，这些模式对于 CMIP6 和 CESM 有很大不同，反映了变化的很大不确定性。此外，对于历史和未来的预测，进行了以大气环流状态为条件的分析。发现给定制度的条件相关性是导致历史时期相关性偏差的主要因素，并与过去和未来的变化相关。这些结果强调了大尺度环流机制的重要性，以及了解它们与与特定变量间相关性相关的局部尺度现象的物理关系的必要性。