Climate and weather extremes such as heat waves, droughts, extreme precipitation events or cold surges have huge social and economic impacts that are expected to increase with climate change. Forecasting of such extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging because of the poor understanding of phenomena that may increase predictability at this time scale. The Madden-Julian Oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales and can also promote or enhance phenomena such as monsoons and hurricanes in other regions of the world. Here we develop artificial neural networks that can lead to a very competitive MJO prediction. While our average prediction skill is about 26-27 days (which competes with that obtained with computationally demanding state-of-the-art climate models), for some initial phases the methodology has a prediction skill of 60 days or longer.

The Madden-Julian Oscillation (MJO) is a major source of predictability on the sub-seasonal (10- to 90-days) time scale. An improved forecast of the MJO, may have important socioeconomic impacts due to the influence of MJO on both, tropical and extratropical weather extremes. Although in the last decades state-of-the-art climate models have proved their capability for forecasting the MJO exceeding the 5 weeks prediction skill, there is still room for improving the prediction. In this study we use Multiple Linear Regression and an Artificial Neural Network as post-processing methods to improve one of the currently best dynamical models developed by the European Centre for Medium-Range Weather Forecast (ECMWF). We show that the post-processing with the machine learning algorithm employed leads to an improvement of the MJO prediction. The largest improvement is in the prediction of the MJO geographical location and intensity.