<![CDATA[Accurate rainfall data are essential for reliable hydrological analysis, yet different measurement methods can yield varying estimates, and the true areal precipitation cannot be directly observed. This study evaluates three rainfall datasets—BMKG observations, Global Precipitation Measurement (GPM), and Automatic Rainfall Recorder (ARR)—using the Extended Triple Collocation (ETC) method in the Cimanceuri Watershed, Indonesia. Daily rainfall data from two representative locations were temporally and spatially collocated for comparative analysis. The results demonstrate location-dependent differences in dataset performance. At Budiarto–Curug, GPM produced the lowest estimated error (RMSE = 7.62 mm), whereas ARR achieved the highest correlation with the unknown rainfall signal (r² = 0.83). At Tangerang–Kresek, ARR showed the best overall performance, with the lowest RMSE (7.26 mm) and highest r² (0.65), while BMKG exhibited the highest error (RMSE = 15.94 mm). Ground-based datasets captured greater rainfall variability, whereas GPM provided smoother estimates. These findings indicate that rainfall dataset performance is influenced by location, spatial variability, and measurement characteristics. ETC offers an objective framework for evaluating rainfall datasets in the absence of a known reference and supports data selection for hydrological modeling and water resources management.]]>
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