The performance of a multi-model composite for seasonal prediction is theoretically examined in terms of a correlation skill. On the basis of theoretical analysis, we discuss the improvement of skill in the multi-model composite using the APCN multi-model seasonal prediction dataset. Although the skill of multi-model composite is generally increased by increasing the number of models, the highest skill can be obtained by selecting several skillful models which are less dependent each other.