The paper introduces and evaluates the "spectral nudging" technique, which is used to force a regional atmospheric model to align with large-scale atmospheric conditions. This method is based on the idea that regional climate statistics are influenced by both large-scale atmospheric conditions and local features like marginal seas and mountain ranges. Unlike traditional boundary forcing techniques, spectral nudging applies forcing not only at the lateral boundaries but also within the integration area, using nudging terms in the spectral domain. The authors demonstrate that this approach successfully keeps the simulated state close to the driving state at larger scales while generating smaller-scale features. They compare the performance of spectral nudging with standard boundary forcing, showing that the latter can lead to internal states in the regional model that conflict with the large-scale state. The paper concludes that spectral nudging can be seen as a suboptimal and indirect data assimilation technique, particularly useful for deriving smaller-scale analyses from global reanalyses or for downscaling climate change scenarios. The study uses the regional climate model REMO and compares its performance with National Centers for Environmental Prediction (NCEP) reanalyses, highlighting the effectiveness of spectral nudging in maintaining consistency with large-scale features while allowing for the development of small-scale details.The paper introduces and evaluates the "spectral nudging" technique, which is used to force a regional atmospheric model to align with large-scale atmospheric conditions. This method is based on the idea that regional climate statistics are influenced by both large-scale atmospheric conditions and local features like marginal seas and mountain ranges. Unlike traditional boundary forcing techniques, spectral nudging applies forcing not only at the lateral boundaries but also within the integration area, using nudging terms in the spectral domain. The authors demonstrate that this approach successfully keeps the simulated state close to the driving state at larger scales while generating smaller-scale features. They compare the performance of spectral nudging with standard boundary forcing, showing that the latter can lead to internal states in the regional model that conflict with the large-scale state. The paper concludes that spectral nudging can be seen as a suboptimal and indirect data assimilation technique, particularly useful for deriving smaller-scale analyses from global reanalyses or for downscaling climate change scenarios. The study uses the regional climate model REMO and compares its performance with National Centers for Environmental Prediction (NCEP) reanalyses, highlighting the effectiveness of spectral nudging in maintaining consistency with large-scale features while allowing for the development of small-scale details.