Skillful prediction of the Indian summer monsoon rainfall (ISMR) at leads of 12–24 months are valuable for farmers and policymakers for water resource management and food security planning. While the ISMR is known to be highly predictable at short leads, estimates of long‐lead “potential skill” is lacking. Here, a new predictor discovery method taking into account simultaneous contributions from all potential drivers unravels a predictor based on the depth of the 20° isotherm (D20) to be best suited for estimating the “potential skill” significantly better than a sea surface temperature (SST)‐based predictor. We find high “potential skills” at 18–24‐months leads instead of at short leads, with the highest skill for 18‐months‐lead forecasts (r = 0.87). Investigation of the intriguing finding reveals that the initial errors for ISMR prediction as well as their growth are controlled by a global manifestation of the El Niño–Southern Oscillation (ENSO) oscillator. Phase‐locking of initial errors of D20 and their growth rates with the annual cycle ascertain that forecast errors are minimum and “potential skill” is maximum at leads of 18–24 months. While non‐linearity of small‐scale D20 anomalies may make realization of the potential predictability challenging, with improvements of coupled models and use of deep‐learning artificial‐intelligence techniques, our findings provide optimism for skillful long‐lead forecasts of ISMR in coming years.

The mesoscale orography over one of the wettest regions of the world makes the Northeast India (NEI) vulnerable to hydrological disasters while sustaining a biodiversity “hotspot.” The monsoon rainy season over the NEI is known to be longer than June–September (JJAS), but an objective definition has been lacking. Understanding the drivers and predictability of rainfall variability over the region is key for sustainable development planning and adaptation to increasing disasters in the backdrop of a warming climate but remained poor due to lack of a working definition of “summer monsoon season” over NEI. Here, using the relationship between rainfall over the region and winds at 850 hPa over North Bay of Bengal (BoB), we provide an objective definition of the monsoon season over NEI and argue that the “summer monsoon rainy season” over the NEI (NEIR) is from May to September (MJJAS). In contrast to a significant negative relationship between JJAS Central India (CI) rainfall and a JJAS El Niño–Southern Oscillation (ENSO) index, the MJJAS rainfall over the NEI has no relationship with MJJAS or JJAS ENSO. Instead, we unravel that (a) the tropical Northwest‐Pacific (TNWP) sea surface temperature (SST), (b) the South Equatorial Indian Ocean Dipole (SEIOD) and (c) the Atlantic Zonal mode (AZM) are potential drivers of the NEIR variability. Using a causal inference algorithm, namely the Peter and Clark Momentary Conditional Independence (PCMCI) method, we show that SEIOD directly influences NEIR while TNWP SST has a two‐way connection with the NEIR. While “internal variability” may be higher over the NEI, significant modulation of the variances of the subseasonal fluctuations by predictable drivers like the Atlantic Niño and North Atlantic water temperature provides optimism for seasonal prediction of the NEIR.

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