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A feature selection technique is used to enhance the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched by wavelet features. The feature selection technique includes a feature similarity selection method and a filter-based feature selection using genetic algorithm (FFSGA). It is employed in this study to find an optimal set of features where redundant and irrelevant features are removed. The entropy index fitness function is used to evaluate the feature subsets. The results show that using the feature selection technique not only improves the equitable threat score by almost 7% at some threshold values for the winter season, but also it extremely decreases the dimensionality. The bias also decreases in both the winter (January and February) and summer (June, July, and August) seasons.