Missingness in rainfall data is one of the well-known and challenging issues faced by meteorologists and researchers from all over the world. The problem would affect the quality of the data which is very important in representing the actual meteorological characteristics of a particular location. Therefore, the missing data should be properly treated in order to provide good quality dataset for the public domain. In furtherance of ensuring the accuracy of imputed missing data, the original structure of the rainfall data series must be specifically preserved when the data are having seasonal patterns. Most of the environmental datasets are generally characterized by outliers and seasonal patterns. These characteristics have certainly affected the performance of missing data imputation methods. The problem of missing data can be treated, but a specific structured approach must be employed when involving dataset that contains outliers and seasonal patterns. This study has highlighted and discussed the structured and comprehensive procedures on how to tackle the problem of missing data by emphasizing on controlled sampling approach for their implementation. The missing values were estimated by using multiple imputation based on block bootstrap approach associated with normal ratio methods compared to the conventional sampling (i.e. general bootstrap approach). The analysis and experimentation are illustrated using several datasets obtained for several locations in Peninsular Malaysia. The block bootstrap approach has revealed its advantage of preserving time series structure in its process and successfully improved the estimates of missing rainfall data imputation.