The analog method (AM) has found application to reconstruct gridded climate fields from the information provided by proxy data and climate model simulations. Here, we test the skill of different setups of the AM, in a controlled but realistic situation, by analysing several statistical properties of reconstructed daily high-resolution atmospheric fields for Northern Europe for a 50-yr period. In this application, station observations of sea-level pressure and air temperature are combined with atmospheric fields from a 50-yr high-resolution regional climate simulation. This reconstruction aims at providing homogeneous and physically consistent atmospheric fields with daily resolution suitable to drive high resolution ocean and ecosystem models. Different settings of the AM are evaluated in this study for the period 1958–2007 to estimate the robustness of the reconstruction and its ability to replicate high and low-frequency variability, realistic probability distributions and extremes of different meteorological variables. It is shown that the AM can realistically reconstruct variables with a strong physical link to daily sea-level pressure on both a daily and monthly scale. However, to reconstruct low-frequency decadal and longer temperature variations, additional monthly mean station temperature as predictor is required. Our results suggest that the AM is a suitable upscaling tool to predict daily fields taken from regional climate simulations based on sparse historical station data.