<![CDATA[Accurate precipitation estimation is essential for hydrological studies, particularly in regions with complex climatic and topographical characteristics, such as Greater Bandung, Indonesia. This study evaluates the performance of two high-resolution global precipitation datasets (GPDs), ERA5 and PERSIANN-CDR, by comparing them against observations from 32 rainfall stations at daily and monthly scales. The evaluation employs a straightforward ranking-based system considering three objective functions: Root Mean Square Error (RMSE), bias (B), and Pearson correlation coefficient (R). The results reveal that ERA5 performs better than PERSIANN-CDR for daily precipitation, as indicated by higher ranking scores across the selected metrics. Interestingly, the opposite is true for monthly precipitation, where PERSIANN-CDR outperforms ERA5. However, while the ranking system offers a practical comparative framework, the objective function values for both datasets exhibit considerable deviations from ground observations. Daily RMSE values reach as high as 24.89 mm, and monthly RMSE peaks at 525.605 mm, alongside less satisfactory bias and correlation scores. These findings suggest that while the ranking system helps identify the more suitable GPD for specific temporal scales, neither dataset is immediately fit for hydrological applications without further refinement. Bias correction of the GPDs against ground-based observations is recommended to improve their reliability and usability in hydrological analyses. This study highlights the need for tailored adjustments to GPDs in regions with high climatic variability.]]>
