Abstract A data‐driven dynamical filter is developed to characterize Madden‐Julian Oscillation (MJO) variability, by representing tropical variability with nonorthogonal empirical‐dynamical modes that allow for constructive and destructive interference. We find that two intraseasonal atmospheric modes, an “MJO‐fast” mode (∼ ${\sim} $45 day period) and a newly identified “MJO‐slow” mode (∼ ${\sim} $70 day period), alongside El Niño‐Southern Oscillation modes that are not entirely removed by temporal filtering, explain nearly all observed Real‐time Multivariate MJO (RMM) index‐based variability. The fastest growing, and most predictable, MJO events are initiated primarily by the MJO‐fast mode over the Indian Ocean, with subsequent progression across the Maritime Continent resulting from destructive and then constructive interference of the MJO‐fast and MJO‐slow modes. These events, which we demonstrate can be identified at forecast initialization time, are shown to be forecasts of opportunity in the ECMWF operational forecast model, with MJO skill extended by roughly a week compared to all other forecasts.

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