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This research aims to evaluate the CHIRPS data in estimating daily rainfall in West Papua compared with automatic weather stations (AWS) data recording. The data used in this research are daily CHIRPS data and AWS daily data recording 1996 to 2020 from AWS Rendani–Manokwari, AWS Jefman–Raja Ampat, AWS Torea–Fakfak, and AWS Kaimana–Kaimana. CHIRPS data were evaluated using the Point to Pixel method based on numerical and categorical parameters i.e., root mean square error (RMSE), mean error (ME), mean absolute error (MAE), Pearson correlation (r), probability of detection (POD), critical success index (CSI), and T-test. The research showed that CHIRPS had a significant difference to AWS data in estimating daily rainfall in West Papua based on a T-test. However CHIRPS has a moderate accuracy in estimating daily rainfall in West Papua with RMSE = 8.59 mm, ME=2.75 mm, and MAE = 5.15 mm and had a moderate positive correlation with AWS data with r= 0.43. Besides, CHIRPS has good accuracy in detecting rain events in West Papua indicated by a POD = 0.72 and CSI = 0.43. Therefore, CHIRPS data can be used as an alternative solution for providing rainfall data in West Papua.   Keywords:  satellite observation, rainfall predictor, point to pixel 

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EVALUASI DATA CLIMATE HAZARDS GROUP INFRARED PRECIPITATION WITH STATION (CHIRPS) DENGAN DATA PEMBANDING AUTOMATIC WEATHER STATIONS (AWS) DALAM MENGESTIMASI CURAH HUJAN HARIAN DI PROVINSI PAPUA BARAT