Based on the method of associating wavelet decomposition with adaptive neurofuzzy inference system (ANFIS), a compositive prediction model on the modality index of the westPacific subtropical high(WPSH) on the monthlyseasonal scale was established. Signals can be freely extended/shrinked in frequency time domain and any passband and passlow frequency branch can be accurately produced and reconstructed by means of wavelet decomposition. Therefore, the complex WPSH modality index time series signals can be separated into several relative simple bandpass signals, which both simplify the system structure and stand out the chief characters of signals. Subsequently, the independent prediction model of the decomposed signals were established based on the advantages of ANFIS model, such as nonlinear, bearingerror, selfadapting and associationlearning and the independent predicted results were integrated finally. The test results showed that under the premise of keeping the main characters of forecast body, the prediction difficulty on WPSH system had effectively been decreased, the precision and durative of the compositive prediction model were evidently improved and promoted compared with that of traditional prediction technique. 