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Drift correction is an important step before using the outputs of decadal prediction experiments and has seen considerable research. However, most drift correction studies consider a relatively small sample of variables and models. Here, we present a systematic application of the existing drift correction strategies for decadal predictions of various sea surface temperature-based metrics from a suite of five state-of-the-art climate models (CanCM4i1, GFDL-CM2.1, HadCM3i2&i3, MIROC5, and MPI-ESM-LR). The best method of drift correction for each metric and model is reported. Preliminary analysis suggests that there is no single method of drift correction that consistently performs best. Initial condition-based drift correction provides the lowest errors in most regions for MIROC5 and the two HadCM3 models, whereas the trend-based drift correction produces lowest errors for CanCM4i1, GFDL-CM2.1, and MPI-ESM-LR over the largest share of the area. There is no merit in using a k-nearest neighbor approach for these drift correction methods. Further, in almost all cases, the multimodel ensemble outperforms the individual models, and thus, the study conclusively suggests using forecasts based on multimodel averages. We also show some additional benefit to be gained by drift correcting each model/metric using their best correction method prior to model averaging and suggest that the results presented here would help potential users expend time and resources judiciously while dealing with outputs from these experiments.