Internal climate variability (ICV) estimates provide a useful benchmark for assessing climate model performance and the emergence of anthropogenically forced climate change. This study aims to quantify the magnitude of ICV using different types of data, representing both Earth System Model simulations and observation-based datasets. We focus on seasonal mean near surface air temperature over Europe utilizing different methodological approaches: assessment of variability inferred from pre-industrial control simulations, spread of a single-model initial-condition large ensemble, separation of uncertainty sources in CMIP6 transient simulations, and forcing attribution in observed time series. Across all methods and datasets, we found that ICV estimates decrease during the seasonal course from winter to autumn and spatially from north-eastern to south-western Europe. By comparing the results of the historical and scenario simulations of the large ensemble and selected CMIP6 models, we conclude that European ICV generally decreases under anthropogenically forced climate change. Moreover, our study suggests that applying ICV estimates as a benchmark for assessing regional climate simulations over Europe should be approached with caution. The estimate based on the pre-industrial control simulations offers an advantage since the simulations are not influenced by external forcings and their ensemble mean estimate encompasses the range of the other methods. When the focus is on future climate simulations, estimates from scenario simulations should be used, as they already account for the influence of anthropogenic forcings on ICV. Regarding ICV estimates from observational data, their advantage lies in accounting for true climate history, free of modelling uncertainty. Historical simulations also account for historical climate change and yield ICV estimates comparable to those from observational data.