Abstract: Global climate models are a keystone of modern climate research. In most applications relevant for decision making, they are assumed to provide a plausible range of possible future climate states. However, these models have not been originally developed to reproduce the regional-scale climate, which is where information is needed in practice. To overcome this dilemma, two general efforts have been made since their introduction in the late 1960s. First, the models themselves have been steadily improved in terms of physical and chemical processes, parametrization schemes, resolution and implemented climate system components, giving rise to the term ?Earth system model?. Second, the global models' output has been refined at the regional scale using limited area models or statistical methods in what is known as dynamical or statistical downscaling. For both approaches, however, it is difficult to correct errors resulting from a wrong representation of the large-scale circulation in the global model. Dynamical downscaling also has a high computational demand and thus cannot be applied to all available global models in practice. On this background, there is an ongoing debate in the downscaling community on whether to thrive away from the ?model democracy? paradigm towards a careful selection strategy based on the global models' capacity to reproduce key aspects of the observed climate. The present study attempts to be useful for such a selection by providing a performance assessment of the historical global model experiments from CMIP5 and 6 based on recurring regional atmospheric circulation patterns, as defined by the Jenkinson?Collison approach. The latest model generation (CMIP6) is found to perform better on average, which can be partly explained by a moderately strong statistical relationship between performance and horizontal resolution in the atmosphere. A few models rank favourably over almost the entire Northern Hemisphere mid-to-high latitudes. Internal model variability only has a small influence on the model ranks. Reanalysis uncertainty is an issue in Greenland and the surrounding seas, the southwestern United States and the Gobi Desert but is otherwise generally negligible. Along the study, the prescribed and interactively simulated climate system components are identified for each applied coupled model configuration and a simple codification system is introduced to describe model complexity in this sense.

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