The seasonal forecast result from as many as sixteen state of the art coupled atmosphere-ocean models using a downscaling component, with respect to observed rainfall estimates, enables the forecasts of each model to be bias corrected to a common 25 km resolution. During the forecast phase, the forecasts from all of these models make use of the downscaling statistics of the training phase and these are next passed on for the construction of a multimodel superensemble. This combination of methods provides a major improvement for the rainfall climatology and anomalies during the forecast phase. A major result of FSU findings is that the long term mean climatology of the superensemble based rainfall is recovered to a correlation of nearly 1.0 with respect to the observed counterpart. This study makes use of the Yatagai tabulations of daily observed gauge rainfall at 25 km resolution. for a 43 year period, and is based on nearly 10,000 sites. The models, in general, do not forecast any heavy rains, in excess of roughly 18 mm/day. That gets accounted for as a systematic error by the bias removed ensemble mean and the superensemble during the training phase. At these locations the observed rains are heavy and are provided at the grid points. The model rains are non zero but are exceedingly small, the bias corrected ensemble mean and the superensemble statistics corrects for this systematic departure of model rains. This high skill for climatology is important for addressing the rainfall anomaly forecasts, those are defined in terms of departures from the observed (rather than a model based) climatology Skill of rainfall anomaly forecasts utilize both deterministic and probabilistic skill measures such as the rms errors, anomaly correlations, equitable threat scores and the Brier skill scores. The skills of the anomaly forecasts are not anywhere near those we noted for the climatology. The multimodel superensemble capitalizes on the persistence of errors of poorer models. The superensemble forecasts of rainfall anomalies invariably carry the highest skills compared to all the member models globally and regionally. Unlike the scatter plot of the superensemble versus the climatogical rains, the behavior of the rainfall anomalies is quite different. For the anomalies the correlations of the observed to the predicted season long rains for the 16 member models range from -0.10 to 0.65. The superensemble is able to elevate that correlation to 0.70. Very similar results were obtained for winter seasons as well. The implication of these results are very significant, i.e. in an operational forecast environment, a priori one might not know, after a forecast is issued, which single model might carry the best forecast for a coming season, however having a superensemble forecast of the rainfall anomaly provides one with some assurance of having the best available forecast. The probabilistic skills show that the superensemble based forecasts carry a much higher reliability score (Briar skill score) compared to all member models. Post processing of multimodel forecasts using ensemble strategies is an important area of future research for the seasonal climate anomaly forecasts.