With the Zebiak-Cane(ZC)model,the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation(CNOP).The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model.By analyzing the behavior of CNOP- type errors,we find that for the normal states and the relatively weak El Nino events in the ZC model,the predictions tend to yield false alarms due to the uncertainties caused by CNOP.For the relatively strong El Nino events,the ZC model largely underestimates their intensities.Also,our results suggest that the error growth of El Nino in the ZC model depends on the phases of both the annual cycle and ENSO.The condition during northern spring and summer is most favorable for the error growth.The ENSO prediction bestriding these two seasons may be the most diffcult.A linear singular vector(LSV)approach is also used to estimate the error growth of ENSO,but it underestimates the prediction uncertainties of ENSO in the ZC model.This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes.CNOP yields the severest prediction uncertainty.That is to say,the prediction skill of ENSO is closely related to the types of initial error.This finding illustrates a theoretical basis of data assimilation.It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill. With the Zebiak-Cane (ZC) model, the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation (CNOP). Results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model.By analyzing the behavior of CNOP-type errors, we find that for the normal states and the relatively weak El Nino events in the ZC model, the predictions tend to yield false alarms due to the uncertainties caused by CNOP.For the relatively strong El Nino events, the ZC model substantial underestimates their intensities. Also, our results suggest that the error growth of El Nino in the ZC model depends on the phases of both the annual cycle and ENSO. condition on northern spring and summer is most favorable for the error growth. The ENSO prediction bestriding these two seasons may be the most diffcult. A linear singular vector (LSV) approach is also used to estimate the error growth of ENSO, but it underestimates the predictor ion uncertainties of ENSO in the ZC model. This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes. CNOP yields the severest prediction of confidence ..That is to say, the prediction skill of ENSO is closely related to the types of initial error. This finding shows a theoretical basis of data assimilation. It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill.