Large socioeconomic impact of the Indian summer monsoon (ISM) extremes motivated numerous attempts at its long range prediction over the past century. However, a rather low potential predictability (PP) of the seasonal ISM, contributed significantly by “internal,” interannual variability was considered insurmountable. Here we show that the internal variability contributed by the ISM subseasonal (synoptic + intraseasonal) fluctuations, so far considered chaotic, is partly predictable as found to be tied to slowly varying forcing (e.g., El Niño and Southern Oscillation). This provides a scientific basis for predictability of the ISM rainfall beyond the conventional estimates of PP. We establish a much higher actual limit of PP (r∼0.82) through an extensive reforecast experiment (1,920 years of simulation) by improving two major physics in a global coupled climate model, which raises a hope for a very reliable dynamical seasonal ISM forecasting in the near future.

The potential predictability of the Indian summer monsoon rainfall (ISMR), soil moisture, and sea surface temperature (SST) is explored in the latest version of the NCEP Climate Forecast System (CFSv2) retrospective forecast at five different lead times. The focus of this study is to find out the sensitivity of the potential predictability of the ISMR to the initial condition through analysis of variance technique (ANOVA), information-based measure, including relative entropy (RE), mutual information (MI), and classical perfect model correlation. In general, the all methods show an increase in potential predictability with a decrease in lead time. Predictability is large over the Pacific Ocean basin as compared to that of the Indian Ocean basin. However, over the Indian land region the potential predictability increases from lead-4 to lead-2 and then decreases at lead-1 followed by again increase at lead-0. While the actual ISMR prediction skill is highest at lead-3 forecast (second highest at lead-1), the potential predictability is highest at lead-2. It is found that highest and second highest actual prediction skill of the ISMR in CFSv2 is due to the combined effects of initial Eurasian snow and SST over Indian, west Pacific and eastern equatorial Pacific Ocean region. While the teleconnection between the ISMR and El Niño-Southern Oscillation is too strong, the ISMR and Indian Ocean dipole have completely out of phase relation in the model as compared to the observation. Furthermore, the actual prediction skill of the ISMR is now very close to the potential predictability limit. Therefore, in order to improve the ISMR prediction skill further, development of model physics as well as improvements in the initial conditions is required.

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Northeast India (NEI), the wettest place on the Earth, has experienced a rapid decrease in summer monsoon rainfall (about 355 mm) in the last 36 years (1979–2014), which has serious implications on the ecosystem and the livelihood of the people of this region. However, it is not clear whether the observed drying is due to anthropogenic activities or it is linked with the global natural variability. A diagnostic model is employed to estimate the amount of recycled rainfall, which suggests that about 7% of the total rainfall is contributed by the local moisture recycling and decrease in recycled rainfall is about 30–50 mm. Using gridded observed rainfall and sea surface temperature data of the last 114 years (1901–2014), here we show that the recent decreasing trend of NEI summer monsoon rainfall is rather associated with the strong interdecadal variability of the subtropical Pacific Ocean. The strong interdecadal variability over NEI suggests a possibility of skillful decadal prediction of the monsoon rainfall, which may have important implications in terms of long‐term planning and mitigation.