This study identifies torrential rainfall patterns in Yogyakarta, Indonesia using multivariate and univariate approaches to propose a statistical model for solving associated issues. First, addressing its long-gap missing rainfall data (approximately 52.8%) is crucial. Therefore, classical imputation methods were enhanced by combining them with the bootstrap algorithm. The hybrid of Random Forest (RF) and the bootstrap algorithm, with the lowest Root Mean Square Error (RMSE) of 7.96 and Mean Absolute Error (MAE) of 0.29, is the best statistical method for imputing Yogyakarta rainfall data missing values. Cluster analysis then classified stations into different rainfall regimes; hierarchical clustering analysis (HCA) recognised four distinct, homogenous regions. The multivariate approach, principal component analysis (PCA), homogenised the rainfall series and optimally reduced long-term rainfall data to validate the HCA analysis results. From the 75% cumulative variation, 14 factors for.....

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