Large-scale fully coupled Earth system models (ESMs) are usually applied in climate projections like the IPCC (Intergovernmental Panel on Climate Change) reports. In these models internal variability is often within the correct order of magnitude compared with the observed climate, but due to internal variability and arbitrary initial conditions they are not able to reproduce the observed timing of climate events or shifts as for instance observed in the El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), or the Atlantic Meridional Overturning Circulation (AMOC). Additional information about the real climate history is necessary to constrain ESMs; not only to emulate the past climate, but also to introduce a potential forecast skill into these models through a proper initialisation. We attempt to do this by extending the fully coupled climate model Max Planck Institute Earth System Model (MPI-ESM) using a partial coupling technique (Modini-MPI-ESM). This method is implemented by adding reanalysis wind-field anomalies to the MPI-ESM's inherent climatological wind field when computing the surface wind stress that is used to drive the ocean and sea ice model. Using anomalies instead of the full wind field reduces potential model drifts, because of different mean climate states of the unconstrained MPI-ESM and the partially coupled Modini-MPI-ESM, that could arise if total observed wind stress was used. We apply two different reanalysis wind products (National Centers for Environmental Prediction, Climate Forecast System Reanalysis (NCEPcsfr) and ERA-Interim reanalysis (ERAI)) and analyse the skill of Modini-MPI-ESM with respect to several observed oceanic, atmospheric, and sea ice indices. We demonstrate that Modini-MPI-ESM has a significant skill over the time period 1980–2013 in reproducing historical climate fluctuations, indicating the potential of the method for initialising seasonal to decadal forecasts. Additionally, our comparison of the results achieved with the two reanalysis wind products NCEPcsfr and ERAI indicates that in general applying NCEPcsfr results in a better reconstruction of climate variability since 1980.