Rainfall modeling with CMIP6-DCPP outputs and local characteristic information using eigenvector spatial filtering varying coefficient (ESF-VC)

Estimating rainfall at a point or region is difficult because complex factors affect rainfall. A helpful strategy is to utilize the GCM output information from CMIP6-DCPP by forming a functional relationship between GCM output data and rainfall data at a certain point or region, called statistical downscaling. However, because the resolution of the GCM output is relatively low, the model could not explain the local effects since the heterogeneity is enormous. Based on this fact, the current research proposes to add some local characteristics in the downscaling model to improve the performance to predict the rainfall levels. Further, the rainfall levels have spatial dependencies among points. Therefore, this research employed the Eigenvector Spatial Filtering-Varying Coefficient (ESF-VC) as the methodology of the modeling. The objective of this research is to perform rainfall predictive modeling with CMIP6-DCPP output and some local characteristic information as predictors using ESF-VC methodology. The approach was implemented to predict the rainfall level in the Province of Riau in Indonesia. Based on the results, the ESF-VC model provides good performance in estimating rainfall in Riau. The variables that provide local effects are altitude, equator (location), equator (distance), and wet month dummy. While the variables ENSO and vegetation (NDVI) have a significant global effect on the model.