We propose a novel climate ‘filter’ approach that employs relative dynamical diagnostic performance to enhance the APCC operational multi-model ensemble (MME) hindcasts of precipitation and temperature at 850 hPa over the East Asian summer monsoon region for the boreal summers of 1981-2003. We also evaluate the performance of individual constituent models and compare the results of corresponding observed and reanalyzed data. The filter is based on the strong relationship between the observed and model-simulated ENSO (NAO)-associated EASM indices. The reproducibility of this relationship is utilized to evaluate model performance. It is found the retrospective forecast skill of a newer type of MME that contains only the ‘more skillful’ models is superior to that of the all-inclusive operational MME. We found that the gap between the skills of MMEs comprising only the more skillful models and those with only the less skillful models significantly widens over the East Asian summer monsoon region. Based on an experimental forecast for two boreal summer seasons, the filtered MME is shown to generally outperform the all inclusive MME, and the proposed method is confirmed to be potentially useful for real time seasonal prediction.

1. Introduction

2. Data, EASM index and climate filter

2.1 Data used

2.2 EASM indices and impacts associated with EASM

2.3 Climate filter

3. Evaluation of the hindcast relationship through the climate filter method

4. Discussion

5. Conclusion