Based on the idea of using historical-analogue information to predict the prediction errors of model, a new method named analogue correction of errors by predictable component （FACEPC） was developed. This method is adopted to identify the predicable components for which the prediction result is relatively not quite sensitive to the initial values. And then for predicable components, an associated scheme is chosen for historical-analogue selection and error correction. This method was further applied to experiments on operational seasonal prediction model of National Climate Center. By selecting suitable analogues and prediction schemes for different regions, the results from cross-validation indicate that the predictive skill scores of summer precipitation and circulation have got significant improvement relative to systematic error correction, which looks more obvious in ENSO episodes and over regions with more predictable components. Especially, the skill scores over China area have also been clearly improved, exhibiting its potential application perspective to operational seasonal prediction. Besides, preliminary sensitive experiments show that the FACEPC-based predictions are also obviously influenced by the analogue-selected factors and the length of historical data.