AbstractThe implementation of a bias‐correction and signal amplification technique to the National Center for Environmental Prediction's Climate Forecast System‐based Grand Ensemble Prediction System Multi‐Model Ensemble outputs is studied for improvements in track predictions of three cyclonic storms over North Indian Ocean. Bias‐correction method involves the removal of lead‐dependent climatological bias from multi‐model ensemble forecasts by using European Centre for Medium‐Range Weather Forecasts Re‐analysis (ERA‐Interim) daily‐averaged data sets as observations. The corrected data are then subjected to signal amplification procedure involving a two‐point space and time correction of ensembles based on the leading signal (ensemble mean), whereby large uncertainties and disagreements between different model outputs are reduced. Results show that bias‐correction and signal amplification technique is, indeed, improving the track forecasts of selected cyclonic storm cases with significant reduction in track errors even at longer lead times.