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This study evaluates the seasonal predictability of the Indian summer monsoon (ISM) rainfall using the Climate Forecast System, version 2 (CFSv2), the current operational forecast model for subseasonal-to-seasonal predictions at the National Centers for Environmental Prediction (NCEP). From a 50-year CFSv2 simulation, 21 wet, dry and normal ISM cases are chosen for a set of seasonal "predictions" with initial states in each month from January to May to conduct predictability experiments. For each prediction, a five-member ensemble is generated with perturbed atmospheric initial states and all predictions are integrated to the end of September. Based on the measures of correlation and root mean square error, the prediction skill decreases with lead month, with the initial states with the shortest lead (May initial states) generally showing the highest skill for predicting the summer mean (June to September; JJAS) rainfall, zonal wind at 850 hPa and sea surface temperature over the ISM region in the perfect model scenario. These predictability experiments are used to understand the finding reported by some recent studies that the NCEP CFSv2 seasonal retrospective forecasts generally have higher skill in predicting the ISM rainfall anomalies from February initial states than from May ones. Comparing the May climatologies generated by the February and May initialized CFSv2 retrospective forecasts, it is found that the latter shows larger bias over the Arabian Sea, with stronger monsoon winds, precipitation and surface latent heat flux. Although the atmospheric bias diminishes quickly after May, an accompanying cold bias persists in the Arabian Sea for several months. It is argued that a similar phenomenon does not occur in the predictability experiments in the perfect model scenario, because the initial shock is negligible in these experiments by design. Therefore, it is possible that the stronger model bias and initial shock in the May CFSv2 retrospective forecasts over the Arabian Sea may be a major factor in affecting ISM prediction skill.