This paper introduces a spectral nudging technique for use in dynamical downscaling of regional climate models. The method imposes large-scale atmospheric states on a regional model, based on the idea that regional climate statistics are influenced by both large-scale atmospheric conditions and regional features such as topography and land-sea distribution. The technique forces the regional model not only at the boundaries but also within the integration area, using spectral nudging terms that efficiently influence large-scale features while leaving smaller-scale features to be determined by the model. The spectral nudging technique is compared to the standard boundary forcing method, which allows the regional model to develop internal states conflicting with the large-scale state. The study shows that spectral nudging keeps the simulated state close to the driving state at larger scales while generating smaller-scale features. It is also shown that the standard boundary forcing technique can lead to inconsistencies in the regional model's internal states. The study uses the REMO regional climate model, forced with NCEP reanalyses. The spectral nudging technique is applied to the zonal and meridional wind components, with a height-dependent nudging coefficient. The results show that the spectral nudging technique improves the representation of local time series compared to NCEP reanalyses and the REMO standard run. The technique also allows the regional model to develop regional and small-scale features superimposed on the large-scale driving conditions. The study demonstrates that the spectral nudging technique is effective in preventing the regional model from deviating from the large-scale state, while allowing for the development of smaller-scale features. The technique is shown to be more effective than the standard boundary forcing method in maintaining consistency between the regional model and the large-scale driving state. The results also indicate that the spectral nudging technique is more efficient in capturing smaller-scale features compared to the standard boundary forcing method. The study concludes that the spectral nudging technique is a suboptimal and indirect data assimilation technique, but it is effective in maintaining consistency between the regional model and the large-scale driving state. The technique is particularly useful for regional climate simulations where the goal is to derive smaller-scale analyses from global reanalyses. The technique is also shown to be effective in capturing smaller-scale features that are not well resolved by the global reanalyses. The study highlights the importance of using the spectral nudging technique in regional climate simulations to improve the accuracy of regional climate models.This paper introduces a spectral nudging technique for use in dynamical downscaling of regional climate models. The method imposes large-scale atmospheric states on a regional model, based on the idea that regional climate statistics are influenced by both large-scale atmospheric conditions and regional features such as topography and land-sea distribution. The technique forces the regional model not only at the boundaries but also within the integration area, using spectral nudging terms that efficiently influence large-scale features while leaving smaller-scale features to be determined by the model. The spectral nudging technique is compared to the standard boundary forcing method, which allows the regional model to develop internal states conflicting with the large-scale state. The study shows that spectral nudging keeps the simulated state close to the driving state at larger scales while generating smaller-scale features. It is also shown that the standard boundary forcing technique can lead to inconsistencies in the regional model's internal states. The study uses the REMO regional climate model, forced with NCEP reanalyses. The spectral nudging technique is applied to the zonal and meridional wind components, with a height-dependent nudging coefficient. The results show that the spectral nudging technique improves the representation of local time series compared to NCEP reanalyses and the REMO standard run. The technique also allows the regional model to develop regional and small-scale features superimposed on the large-scale driving conditions. The study demonstrates that the spectral nudging technique is effective in preventing the regional model from deviating from the large-scale state, while allowing for the development of smaller-scale features. The technique is shown to be more effective than the standard boundary forcing method in maintaining consistency between the regional model and the large-scale driving state. The results also indicate that the spectral nudging technique is more efficient in capturing smaller-scale features compared to the standard boundary forcing method. The study concludes that the spectral nudging technique is a suboptimal and indirect data assimilation technique, but it is effective in maintaining consistency between the regional model and the large-scale driving state. The technique is particularly useful for regional climate simulations where the goal is to derive smaller-scale analyses from global reanalyses. The technique is also shown to be effective in capturing smaller-scale features that are not well resolved by the global reanalyses. The study highlights the importance of using the spectral nudging technique in regional climate simulations to improve the accuracy of regional climate models.