Indian summer monsoon (ISM) is an important component of the tropical climate system, known for its regular seasonality and abundance of rainfall over the country. The droughts and floods associated with the year-to-year variation of the average seasonal rainfall have devastating effect on people, agriculture and economy of this region. The demand for prediction of seasonal monsoon rainfall, therefore, is overwhelming. A number of attempts to predict the seasonal mean monsoon have been made over a century, but neither dynamical nor empirical models provide skillful forecasts of the extremes of the monsoon such as the unprecedented drought of 2002. This study investigates the problems and prospects of extended range monsoon prediction. An evaluation of the potential predictability of the ISM with the aid of an ensemble of Atmospheric General Circulation Model (AGCM) simulations indicates that the interannual variability (IAV) of ISM is contributed equally by the slow boundary forcing (‘externally’ forced variability) and the inherent climate noise (‘internal’ variability) in the atmosphere. Success in predicting the ISM would depend on our ability to extract the predictable signal from a background of noise of comparable amplitude. This would be possible only if the ‘external’ variability is separable from the ‘internal’ variability. A serious effort has been made to understand and isolate the sea surface temperature (SST) forced component of ISM variability that is not strongly influenced by the ‘internal’ variability. In addition, we have investigated to unravel the mechanism of generation of ‘internal’ IAV so that the method of isolating it from forced variability may be found. Since the primary forcing mechanism of the monsoon is the large-scale meridional gradient of deep tropospheric heat sources, large-scale changes in tropospheric temperature (TT) due to the boundary forcing can induce interannual variations of the timing and duration of the monsoon season. The concept of interannually varying monsoon season is introduced here, with the onset and withdrawal of monsoon definitions based on the reversal of meridional gradient of TT between north and south. This large scale definition of the monsoon season is representative of the planetary scale influence of the El Ni˜no Southern Oscillation (ENSO) on monsoon through the modification of TT and the cross equatorial pressure gradient over the ISM region. A sig- nificant relationship between ENSO and monsoon, that has remained steady over the decades, is discovered by which an El Ni˜no (La Ni˜na) delays (advances) the onset, advances (delays) the withdrawal and suppresses (enhances) the strength of the monsoon. The integral effect of the meridional gradient of TT from the onset to withdrawal proves to be a useful index of seasonal monsoon which isolates the boundary forced signal from the influence of internal variations that has remained steady even in the recent decades. However, consistent with the estimates of potential predictability, the boundary forced variability isolated with the above definitions explains only about 50% of the total interannual variability of ISM. Detailed diagnostics of the onset and withdrawal processes are performed to understand how the ENSO forcing modifies the onset and withdrawal, and thus the seasonal mean monsoon. It is found that during an El Ni˜no, the onset is delayed due to the enhanced adiabatic subsidence that inhibits vertical mixing of sensible heating from the warm landmass during pre-monsoon months, and the withdrawal is advanced due to the horizontal advective cooling. This link between ENSO and monsoon is realized through the advective processes associated with the stationary waves in the upper troposphere set up by the tropical ENSO heating. The remaining 50% of the monsoon IAV is governed by internal processes. To unravel the mechanism of the generation of internal IAV, we perform another set of AGCM simulations, forced with climatological monthly mean SSTs, to extract the pure internal IAV. We find that the spatial structure of the intraseasonal oscillations (ISOs) in these simulations has significant projection on the spatial structure of the seasonal mean and a common spatial mode governs both intraseasonal and interannual variability. Statistical average of ISO anomalies over the season (seasonal ISO bias) strengthens or weakens the seasonal mean. It is shown that interannual anomalies of seasonal mean are closely related to the seasonal mean of intraseasonal anomalies and explain about 50% of the IAV of the seasonal mean. The seasonal mean ISO bias arises partly due to the broadband nature of the ISO spectrum, allowing the intraseasonal time series to be aperiodic over the season and partly due to a non-linear process where the amplitude of ISO activity is proportional to the seasonal bias of ISO anomalies. The later relationship is a manifestation of the binomial character of the rainfall time series. The remaining part of IAV may arise due to the complex land-surface processes, scale interactions, etc. We also find that the ISOs over the ISM region are not significantly modulated by the Pacific and Indian Ocean SST variations. Thus, even with a perfect prediction of SST, only about 50% of the observed IAV of ISM could be predicted with the best model in forced mode. Even so, prediction of all India rainfall (AIR) representing the average conditions of the whole country and the season may not always serve the purposes of monsoon forecasting. One reason is the large inhomogeneities in the rainfall distribution during a normal seasonal monsoon. Agriculture and hydrological sector could benefit more if provided with regional scale forecasts of active/break spells 2-3 weeks ahead. Therefore, we advocate an alternative strategy to the seasonal prediction. Here, we present a method to estimate the potential predictability of active and break conditions from daily rainfall and circulation from observations for the recent 24 years. We discover that transitions from break to active conditions are much more chaotic than those from active to break, a fundamental property of the monsoon ISOs. The potential predictability limit of monsoon breaks (∼20 days) is significantly higher than that of the active conditions (∼10 days). An empirical real- time forecasting strategy to predict the sub-seasonal variations of monsoon up to 4 pentads (20 days) in advance is developed. The method is physically based, with the consideration that the large-scale spatial patterns and slow evolution of monsoon intraseasonal variations possess some similarity in their evolutions from one event to the other. This analog method is applied on NOAA outgoing longwave radiation (OLR) pentad mean data which is available on a near real time basis. The elimination of high frequency variability and the use of spatial and temporal analogs produces high and useful skill of predictions over the central and northern Indian region for a lead-time of 4-5 pentads. An important feature of this method is that, unlike other empirical methods to forecast monsoon ISOs, this uses minimal time filtering to avoid any possible end-point effects, and hence it has immense potential for real-time applications.