Result: Uncovering Rainfall Trend Discrepancies in Brazil (1983–2022): Insights From Ground‐Based and Blended Datasets.

Global assessments have exposed considerable inconsistencies among precipitation datasets, particularly over regions with complex terrain or sparse observation networks. These uncertainties are especially critical in Brazil, where climatic diversity, topographic complexity, and uneven gauge distribution make reliable rainfall monitoring challenging. This study conducts a comprehensive evaluation of rainfall trends and distributional similarity across Brazil using five long‐term gridded datasets—gauge‐based (BR‐DWGD), satellite‐gauge blends (CHIRPS and PERSIANN‐CDR), and reanalysis products (ERA5‐Land and MERRA‐2)—and for four precipitation indices: PRCPTOT, R20, R99PTOT, and TDD. We assess the similarity between the gauge‐based BR‐DWGD and the other four gridded precipitation products using normalised Mann–Whitney U statistics. Regarding temporal distribution, CHIRPS shows the highest agreement with BR‐DWGD (U = 1.00 for PRCPTOT, R99PTOT and TDD), while ERA5‐Land performs better for R20 (U = 0.59) but shows moderate to low agreement for other indices (U = 0.25–0.47). PERSIANN‐CDR and MERRA‐2 exhibit poor similarity, with U values generally below 0.12 and close to zero, respectively. However, trend detection using the Mann–Kendall test with Trend‐Free Pre‐Whitening and False Discovery Rate correction reveals substantial regional disparities, with MERRA‐2 and ERA5‐Land partially replicating BR‐DWGD's drying trends for total precipitation (U = 0.83 and U = 0.69, respectively), but identifying more extensive significant trends, especially in Southeast and Northern Brazil. Conversely, CHIRPS and PERSIANN‐CDR detect few significant trends. Factors such as gauge density and satellite‐based corrections influence dataset performance. Despite limitations, annual trends offer valuable insights into precipitation alterations over the past four decades in Brazil, with implications for climate change impacts and regional hydrological cycles. However, the significance of trends varies spatially, underscoring the need for careful dataset selection in regional climatological studies and highlighting the persistent challenges of representing rainfall trends in tropical and subtropical climates using global gridded products. [ABSTRACT FROM AUTHOR]

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