This study compares the simulation and prediction skill of the Indian summer monsoon at two different horizontal resolutions viz., T126 (~100 km) and T382 (~38 km) using 28 years of hindcast runs of the National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) model. It is found that the simulation of the mean state of the South Asian summer monsoon, its variance, and prediction skill of the all India summer monsoon rainfall (AISMR) are better represented in the high-resolution configuration (T382) of the CFSv2 compared to the low-resolution (T126) configuration. In the high-resolution run, the systematic bias in the teleconnection between the AISMR and Indian Ocean Dipole (IOD) has considerably reduced and the teleconnections between the AISMR and El Niño-Southern Oscillation (ENSO) remained same. We hypothesize that the better simulation of mean climate and IOD-AISMR teleconnection in highresolution configuration (T382) of CFSv2 are responsible for the improved prediction skill of AISMR in T382 configuration. Although the T382 configuration of CFSv2 has shown a significant improvement in the simulation and prediction of Indian summer monsoon as compared to the T126 configuration, several parallel efforts are still essential to understand the processes controlling some of the systematic biases of CFSv2 and those efforts are underway as part of the Monsoon Mission project.

Using carefully designed coupled model experiments, we have demonstrated that the prediction skill of the all India summer monsoon rainfall (AISMR) in Climate Forecast System version 2 (CFSv2) model basically comes from the El-Niňo Southern Oscillation-Monsoon teleconnection. On the other hand, contrary to observations, the Indian Ocean coupled dynamics do not have a crucial role in controlling the prediction skill of the AISMR in CFSv2. We show that the inadequate representation of the Indian Ocean coupled dynamics in CFSv2 is responsible for this dichotomy. Hence, the improvement of the Indian Ocean coupled dynamics is essential for further improvement of the AISMR prediction skill in CFSv2.

Monsoon onset is an inherent transient phenomenon of Indian Summer Monsoon and it was never envisaged that this transience can be predicted at long lead times. Though onset is precipitous, its variability exhibits strong teleconnections with large scale forcing such as ENSO and IOD and hence may be predictable. Despite of the tremendous skill achieved by the state-of-the-art models in predicting such large scale processes, the prediction of monsoon onset variability by the models is still limited to just 2–3 weeks in advance. Using an objective definition of onset in a global coupled ocean-atmosphere model, it is shown that the skillful prediction of onset variability is feasible under seasonal prediction framework. The better representations/simulations of not only the large scale processes but also the synoptic and intraseasonal features during the evolution of monsoon onset are the comprehensions behind skillful simulation of monsoon onset variability. The changes observed in convection, tropospheric circulation and moisture availability prior to and after the onset are evidenced in model simulations, which resulted in high hit rate of early/delay in monsoon onset in the high resolution model.

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Indian Summer Monsoon (ISM) synoptic scale systems (low‐pressure systems, LPS) are known to produce increased rainfall over central India (CI). Fidelity of the Climate Forecast System version 2 (CFSv2) at simulating the LPS and their characteristics is evaluated in this study using a feature tracking algorithm. The model is able to reproduce the clustering of LPS by monsoon intraseasonal oscillations and the associated precipitation over eastern‐central India. It is found that mean biases in circulation and moisture stem from cold sea surface temperature (SST) bias in the model which results in weak LPS linked rainfall events over central India. Two sensitivity experiments were carried out to study the effect of coupled dynamics of tropical basins on LPS. Suppression of active dynamics of the tropical Indian Ocean in CFSv2 causes a reduction in cold SST bias and enhanced cyclogenesis in the northern Bay of Bengal. The reduced low‐level anticyclonic bias and enhanced moisture availability result in a better simulation of LPS structure, and associated precipitation over CI. Suppression of active ocean dynamics in tropical Pacific Ocean causes a perennial El‐Niño type bias which restricts LPS propagation over the Indian landmass, possibly due to time‐mean subsidence induced by remote El‐Niño forcing. Sensitivity experiments indicate the need for improvements in the representation of tropical Indian Ocean coupled dynamics as well as convective parameterization schemes in the model for subsequent improvements in the simulation of ISM at various time scales.