Prosody plays a vital role for conveying both communicative meanings and specific speaking styles in speech communication. In recent years, Hidden Markov Model (HMM)-based synthesis system (HTS) has been developed in triumph, which can synthesize stable and smooth speech. However, the prosody of the synthesized speech suffers from the over-smoothing problem. Thus, a better prosodic model is required to improve the natural variability of the synthesized speech. This study exploits a hybrid method to alleviate this problem by combining the statistical and the template-based unit selection methods. First, a two-level clustering approach is proposed to obtain representative prosodic patterns (denoted by codewords) of the hierarchical prosodic structure modeled by a modified Fujisaki model. The prosodic codewords are then used to represent the prosody of each sentence in the parallel corpus consisting of the real speech corpus and the synthesized counterpart obtained from the HTS. The synthesized speech utterance is then used as the query for retrieving the prosodic codewords of the utterances in the synthesized corpus. The retrieved synthesized prosodic codewords are mapped to the prosodic codewords of the real speech based on linear mapping rules obtained from the parallel corpus. The prosodic codeword language models for prosodic word and prosodic phrase are employed respectively to choose the optimal codeword sequence of the real speech. Finally, the most likely sequence of prosodic codewords can be obtained based on the NURBS-based continuity measure for synthesizing speech with natural prosody. The experimental results of subjective and objective tests demonstrate that the proposed prosodic model substantially improves naturalness of the intonation of the synthesized speech compared to that of the HMM-based method.