Rainfall regionalization in Thailand based on statistically validated clustering and its application to spatial rainfall interpolation

Rainfall regionalization plays an essential role in identifying homogeneous rainfall patterns and supporting hydrological and climate analyses. In Thailand, the regionalization adopted by the Thai Meteorological Department (TMD) is primarily based on monsoon wind systems and broad geographic boundaries, which may not adequately represent sub-regional variability in rainfall behavior. This study proposes a data-driven framework to identify homogeneous rainfall regions using monthly rainfall observations from 67 stations across Thailand over the period 1983-2018. Two representations are examined: a standardized representation and a principal component analysis (PCA)-based standardized representation. K-means clustering is applied to both representations, and the resulting clusters are then evaluated using L-moment homogeneity testing, principal component visualization, and statistical and spatial validity indices. The PCA-standardized dataset produces clusters with improved separation and stronger homogeneity relative to the standardized dataset, and the resulting rainfall regions are further interpreted in terms of their physical rainfall characteristics. The practical relevance of the identified regions is further demonstrated through leave-one-out cross-validation comparing inverse distance weighting (IDW), K-nearest-neighbor IDW, and cluster-based IDW approaches using the proposed regions, existing data-driven regions, and TMD regions. The proposed cluster-based IDW approach achieves interpolation error reductions of approximately 9.18-11.55% compared with conventional IDW- and TMD-based alternatives, while providing performance comparable to that obtained using other recent data-driven regional classifications.