Forecasting precipitation as a major component of the hydrological cycle is of primary importance in water resources engineering, planning and management as well as in scheduling irrigation practices. In this study, the abilities of hybrid wavelet-genetic programming [i.e., wavelet-gene-expression programming, WGEP] and wavelet-neural networks (WANN) models for daily precipitation forecasting were investigated. In the first step, the single GEP and ANN models were established to forecast daily precipitation values using the previously recorded values. The results present low accuracy. In the next step, the hybrid WGEP and WANN models were used by introducing the wavelet coefficients as GEP and ANN input vectors. The results showed that the hybrid WGEP and WANN models produced more accurate simulations than the single models.