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.

In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

Along with good prediction skill for major SST boundary forcings such as El Niño and IOD, their appropriate teleconnection spatial patterns also need to be captured well for the better prediction of the land precipitation like Indian summer monsoon rainfall. Here in the study, even though majority of the models has better skill for Nino3.4 index and IOD index, their spatial teleconnection pattern is higher for CFSv2‐T382 (pattern correlation of 0.8) and also has less bias in tropical region. Thus as seen in the figure, it has better Indian summer monsoon rainfall (ISMR)–SST relationship (PCC = 0.6) compared to all other models and hence CFSv2‐T382 has better skill (0.55) for ISMR, while skill is less than 0.1 for the models with PCC values very less. Spatial pattern of correlation between ISMR and seasonal SST anomalies from (a) observations, (b)–(p) individual model hindcasts and (o) MME of all models. Statistically significant (90% confidence level) correlations are stippled.

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