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The active-break cycle of monsoons is the most important phenomenon of the Indian summer monsoon, and its prediction in real time will help to review the ongoing monsoon conditions for providing outlooks to farmers and disaster managers. The monsoon seasons of 2017 and 2018 during July–August witnessed many active-break-active transitions, with long dry spells. The recently implemented Climate Forecast System version 2 coupled model-based ensemble prediction system at the India Meteorological Department was used to observe the performance of a real-time extended-range forecast (ERF) of active-break-active cycles of the monsoon at different spatial scales during 2017 and 2018. The results indicated that the operational ERF has good fidelity in predicting the active-break-active transitions of the monsoon during 2017 and 2018 based on large-scale indices including the monsoon intraseasonal oscillation and low-level circulation. Quantitatively, the forecast of all India and central India rainfall during the active, break and transition phases of the monsoon are very well captured with a lead time of 2–3 weeks. In particular, the long dry spell during first 3 weeks of August and the active last week of August 2017 associated with very heavy rainfall over western coastal states of India were very well captured, whereas the active phase over the southern peninsula during 10–16 August 2018 was slightly underestimated. At smaller spatial scales, the ERFs for 36 meteorological (met)-subdivisions of India for 2–3 weeks could provide useful guidance to farmers during different monsoon phases. Thus, the skilful real-time ERF of the monsoon is very useful to user communities.

Eastward propagating Madden–Julian Oscillation (MJO) is a dominant mode of the intraseasonal variability and hence a potential source of intraseasonal predictability. Therefore, advancing MJO prediction using state-of-the-art dynamical model is of utmost importance for improving intraseasonal prediction. The prediction skill and predictability of MJO are assessed using 44 members ensemble hindcast (16 years data; 2001–2016) of CFSv2 based extended range prediction (ERP) system of IITM as well as 10 member ensemble hindcast (16 years data; 2001–2016) of ECMWF S2S dataset. The MJO is diagnosed using a newly developed Extended Empirical Orthogonal Function (EEOF) analysis. Near equatorial (15° S–15° N) model anomaly fields are projected onto the leading pair of observed eigen modes. The leading pair of observed eigen modes are obtained based on the EEOF analysis of the combined field of zonal wind at 200 hPa (U200), zonal wind at 850 hPa (U850) and velocity potential at 200 hPa (chi200). Model forecasted principal components (PCs) are quantitatively compared with observed PCs using bivariate correlation coefficient and root mean square error (RMSE). We find that MJO could be predicted up to around 22 days (around 31 days) for IITM ERP system (ECMWF S2S dataset) as measured by anomaly correlation coefficient remains larger than 0.5 and RMSE remains lower than 1.4. This prediction skill is quite low compared to potential predictability, which is estimated as more than 40 days both for IITM-ERP and ECMWF system. MJO prediction skill varies with initial MJO phase, particularly at the longer lead. This variation is more significant for the ECMWF system. Model (both for IITM-ERP and ECMWF) predicted amplitude drops at a faster rate and phase propagation speed for almost all initial phase is slower and amplitude is weaker compared to the observation. It could be concluded that even the state-of-the-art models [IITM-ERP (basically NCEP CFSv2) and ECMWF] are also not free from systematic errors/biases. Hence, there is an enormous space for improving MJO prediction skill by reducing these errors/biases in the dynamical model and error in the initial condition.

Several methods are available to track the intraseasonal oscillation, namely the Madden–Julian oscillation (MJO) and monsoon intraseasonal oscillation (MISO). However, no methods to track both the modes in a uniform framework for real-time application exist. A new method to track the smooth propagation of the MJO and MISO is proposed to use it for real-time monitoring. The new approach is based on extended empirical orthogonal function (EEOF) analysis of the combined field of meridionally averaged zonal wind at 850 hPa (U850), zonal wind at 200 hPa and the velocity potential at 200 hPa. The EEOF method does not merely capture the MJO but also smooths (i.e., filters out undesired modes) the temporal propagation in the phase space defined by first two leading principal components (PCs) of the EEOF. Using these leading PCs, we additionally introduce a seasonally varying regression coefficient to filter out the space time structure of the MJO in various fields (rainfall/outgoing long-wave radiation, etc.). These reconstructed fields are then used to link the bidirectional phase movement (i.e., northward propagating MISO and eastward propagating MJO) during boreal summer. The life cycle of the MJO and MISO could be captured with equal fidelity with this current method. MJO filtering could be carried out for any space–time data, not limited to the variables/fields used in EEOF analysis. Examples of the TOGA-COARE and DYNAMO period MJO as well as a few strong MISO events are discussed to check the robustness of the newly proposed method.