A Skillful Prediction of Monsoon Intraseasonal Oscillation Using Deep Learning

Abstract The northward‐propagating 30–60 days mode of monsoon rainfall anomalies over India, commonly referred to as the monsoon intraseasonal oscillation (MISO), plays a critical role in driving the active and break spells over the monsoon zone of the country. These oscillations are essential to understanding and predicting the variability of the Indian summer monsoon, which has significant implications for agriculture and water management. This study uses daily precipitation data from the TRMM/GPM satellite to derive MISO indices (MISO1 and MISO2). These indices were obtained through an extended empirical orthogonal function analysis conducted on 25 years of daily rainfall anomalies over the Indian region. The long time series of MISO1 and MISO2 indices generated from this analysis were then used to forecast future values using a transformer‐based deep learning model. The deep learning model demonstrated skilful predictions of the MISO indices for 2018–2022, with forecast lead times extending to 18 days. Notably, the model outperformed conventional operational numerical weather prediction models in predicting the MISO indices. These results indicate the potential for more reliable sub‐seasonal to seasonal (S2S) predictions of the Indian monsoon. The findings from this work highlight the effectiveness of using advanced deep learning techniques, such as Transformer architectures, in enhancing the predictability of complex atmospheric phenomena like MISO, thereby improving the outlook for monsoon forecasting.