Statistical Evaluation of Observed Precipitation from INMET Meteorological Stations and MERGE Estimates in the Eastern Amazon

Accurate precipitation data are essential for understanding hydrological processes and supporting environmental and water resource management in the Amazon, where observational networks remain sparse and spatially uneven. This study evaluates the performance of the MERGE (Merge of Satellite and Gauge Precipitation Data) dataset, developed by CPTEC/INPE, in representing rainfall variability in the Eastern Amazon. Daily precipitation data from five INMET meteorological stations were compared with MERGE estimates over a 20-year period (1998–2017) using a multi-metric statistical framework, including correlation, regression, error metrics, efficiency indices, and clustering analysis. The results indicate strong agreement between observed and estimated precipitation, with Pearson correlation coefficients ranging from 0.94 to 0.99 and Nash–Sutcliffe efficiency values between 0.87 and 0.97. Regression analyses show coefficients of determination between 0.89 and 0.98, indicating that MERGE effectively reproduces the magnitude and temporal variability of precipitation. Monthly and interannual analyses confirm consistent representation of seasonal patterns and rainfall dynamics across the evaluated stations. The boxplot analysis reveals that MERGE accurately captures the overall distribution of precipitation but tends to underestimate higher precipitation values, particularly during months associated with intense rainfall. This behavior reflects limitations in representing localized convective events and spatial variability. Overall, the results demonstrate that MERGE provides a reliable representation of precipitation variability in the Eastern Amazon and represents a valuable dataset for hydroclimatic analyses in regions with limited observational coverage.