STATISTICAL MODELING FOR DOWNSCALING USING PRINCIPAL COMPONENT REGRESSION AND DUMMY VARIABLES: A CASE OF SIAK DISTRICT - Arisman Adnan; Elsa Riesta Alika; Divo Dharma Silalahi; Felia Rizki Aulia; Gustriza Erda (PATTIMURA UNIVERSITY, 2026)
Indonesia, as a tropical country, is characterized by two primary seasons: the rainy season and the dry season. It is evident that meteorological shifts can exert considerable influence on the agricultural sector, a notable example being the cultivation of palm oil. Consequently, the ability to predict rainfall has emerged as a pivotal element in the broader endeavor to mitigate the adverse effects of climate change. This study employs statistical downscaling using the Principal Component Regression (PCR) approach to model rainfall predictions. The issue of multicollinearity, a common occurrence in Global Circulation Model (GCM) data, is addressed through the use of Principal Component Regression (PCR). This method has been demonstrated to stabilize the model structure and reduce variance in the regression coefficients. The data utilized encompass observed rainfall from LIBO Estate, which is owned by PT SMART Tbk (SMART Research Institute), for the period from 2013 to 2022. This data serves as the response variable, while the CMIP6 GCM simulation output data functions as the predictor variable. The findings indicated that the initial PCR model exhibited an RMSE value ranging from 97.06 to 131.69, along with an R² value ranging from 14.25% to 20.49%. The incorporation of dummy variables into the model resulted in a substantial enhancement in its performance, as evidenced by a decline in RMSE to 24.46–35.83 and an increase in R² to 89.02%–90.24%. The findings indicate that the use of PCR with dummy variables is an effective approach for enhancing the accuracy of rainfall modeling through statistical downscaling.