The objective of this study is to evaluate and compare the proposed statistical downscaling model in Kelantan and Terengganu states. The study also investigates the most accurate imputation methods in handling the missing atmospheric data and the important predictors for a statistical downscaling method by reducing the dimensionality data. The data used in this study include atmospheric data (predictors) and daily rainfall data (predictand) from 1998 until 2007. As part of its methodology, this study had used an imputation method for handling missing data. Then, Principal Component Analysis (PCA) was applied to rectify the issue of high-dimensional data and select predictors for a two-phase model. The two-phase machine learning techniques were introduced as a precise statistical downscaling method in Kelantan and Terengganu states. The first phase is a classification using the Support Vector Classification (SVC) that determines dry and wet days. Subsequently, a regression e.....

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