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While previous studies suggested that salinity could feed back onto MJO variability via modulating upper ocean stratification and further on SST, there is no direct evidence yet proving (or disproving) the importance of this feedback in MJO evolution and its predictability. This study is an initial attempt to quantify the role of SSS feedback on MJO predictability, based on a "perfect model" framework with the CFSv2. Specifically, the SSS feedback is isolated by nudging model SSS to climatological states during forecasts. For comparison, two more experiments were done, one as a benchmark experiment by estimating MJO predictability in CFSv2 and another one for estimating the role of SST feedback. Analyses of these experiments indicate that SSS feedback exerts negligible influences on MJO predictability within the constraints of the model, in contrast to significant impacts from SST feedback. Further analysis showed that a lack of SSS influence in MJO predictability can be attributed to marginal changes in SST associated with the SSS nudging. However, there is a caveat to the conclusion about SSS feedback. Because the barrier layer (BL) acts as a "bridge" for possible SSS influences on SST over the tropical Indian and western Pacific oceans, its simulation in CFSv2 is further explored. Analyses indicate that, in spite of realistic simulations of the MJO and intraseasonal SSS variability in CFSv2, significant BL simulation biases are present in the tropical oceans, including too thin a climatological thickness, too small intraseasonal variations, and an unrealistic intraseasonal BL-SST relationship. Thus, our predictability experiments cannot reject the hypothesis that SSS does play a role in MJO predictability; it is possible that biases in CFSv2 influence its ability to capture such signals.