This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

Statistical downscaling (SDS) is one of the developing models for rainfall estimation. The SDS model is a regression model used to analyze the relation of global (GCM output) and local data (rainfall). Rainfall has large variance so that clustering is needed to minimize the variance. One of the analytical methods that can be used in clustering rainfall estimation is cluster wise regression. There are three Methods for Clusterwise regression namely Linear Regresion, Finite Mixture Method (FMM) and Cluster-Weighted Method (CWM). This study used GCM outputs data namely CFRSv2 as a covariate. The response variable is rainfall data in four stations such as Bandung, Bogor, Citeko and Jatiwangi from BMKG. The purpose of this study is to increase the accuracy of rainfall estimation using the three methods and compare the clusterwise regression with PCR and PLS models. Based on the value of RMSEP, the clusterwise regression with FMM was the best method to estimate rainfall in four stations.