Continuous data assimilation (CDA) is successfully implemented for the first timefor efficient dynamical downscaling of a global atmospheric reanalysis. A com-parison of the performance of CDA with the standard grid and spectral nudgingtechniques for representing long- and short-scale features in the downscaled fieldsusing the Weather Research and Forecast (WRF) model is further presented andanalysed. The WRF model is configured at 0.25◦×0.25◦horizontal resolution andis driven by 2.5◦×2.5◦initial and boundary conditions from NCEP/NCAR reanal-ysis fields. Downscaling experiments are performed over a one-month period inJanuary 2016. The similarity metric is used to evaluate the performance of thedownscaling methods for large (2,000 km) and small (300 km) scales. Similarityresults are compared for the outputs of the WRF model with different downscalingtechniques, NCEP/NCAR reanalysis, and NCEP Final Analysis (FNL, available at0.25◦×0.25◦horizontal resolution). Both spectral nudging and CDA describe betterthe small-scale features compared to grid nudging. The choice of the wave number iscritical in spectral nudging; increasing the number of retained frequencies generallyproduced better small-scale features, but only up to a certain threshold after which itssolution gradually became closer to grid nudging. CDA maintains the balance of thelarge- and small-scale features similar to that of the best simulation achieved by thebest spectral nudging configuration, without the need of a spectral decomposition.The different downscaled atmospheric variables, including rainfall distribution, withCDA is most consistent with the observations. The Brier skill score values furtherindicate that the added value of CDA is distributed over the entire model domain.The overall results clearly suggest that CDA provides an efficient new approach fordynamical downscaling by maintaining better balance between the global model andthe downscaled fields