Skillful Prediction of Indian Monsoon Intraseasonal Precipitation Using Central Indian Ocean Mode and Machine Learning

Abstract

Monsoonal precipitation is dominated by intraseasonal variabilities, whose skillful prediction lead time is currently less than 5 days and remains a grand challenge. Here we show that an intrinsic variability in the Indian Ocean, the Central Indian Ocean (CIO) mode, when combined with a machine learning (ML) algorithm, can produce skillful predictions of intraseasonal precipitation over the monsoon region with a lead time of over 15 days, which is close to the theoretical predictability limit. This remarkable skill improvement stems from the fact that the CIO mode is dynamically related to the intraseasonal monsoon rainfall, while the data-driven ML algorithm suppresses unwanted high-frequency noise. Using the CIO mode and the ML algorithm, the forecast system hybridizes physical fundamentals and versatility of data-driven algorithms. The identification of CIO mode and the verification of its significant contribution to intraseasonal predictions advance our understanding of the coupled monsoon system and also underscores the great potential of ML techniques in weather forecasts and climate predictions.