Theoretical examination of a multi‐model composite for seasonal prediction

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.