  We analyse Indian summer monsoon (ISM) seasonal reforecasts by CFSv2 model,
initiated from January (4-month lead time, L4) through May (0-month lead time,
L0) initial conditions (ICs), to examine the cause for highest all-India ISM
rainfall (ISMR) forecast skill with February (L3) ICs. The reported highest L3
skill is based on correlation between observed and predicted interannual
variation (IAV) of ISMR. Other scores such as mean error, bias, RMSE, mean,
standard deviation and coefficient of variation, indicate higher or comparable
skill for April(L1)/May(L0) ICs. Though theory suggests that forecast skill
degrades with increase in lead-time, CFSv2 shows highest skill with L3 ICs, due
to predicting 1983 ISMR excess for which other ICs fail. But this correct
prediction is caused by wrong forecast of La Nina or cooling of equatorial
central Pacific (NINO3.4) during ISM season. In observation, normal sea surface
temperatures (SSTs) prevailed over NINO3.4 and ISMR excess was due to variation
of convection over equatorial Indian Ocean or EQUINOO, which CFSv2 failed to
capture with all ICs. Major results are reaffirmed by analysing an optimum
number of 5 experimental reforecasts by current version of CFSv2 with
late-April/early-May ICs having short yet useful lead-time. These reforecasts
showed least seasonal biases and highest ISMR correlation skill if 1983 is
excluded. Model deficiencies such as over-sensitivity of ISMR to SST variation
over NINO3.4 (ENSO) and unrealistic influence of ENSO on EQUINOO, contribute to
errors in ISMR forecasting. Whereas, in observation, ISMR is influenced by both
ENSO and EQUINOO. Forecast skill for Boreal summer ENSO is found to be
deficient with lowest skill for L3/L4 ICs, hinting the possible influence of
long lead-time induced dynamical drift. The results warrant the need for
minimisation of bias in SST boundary forcing to achieve improved ISMR
forecasts.
