Satellite and Reanalysis Dataset Dependence of ETCCDI Extreme Precipitation Trends in Paraná, Southern Brazil

This study examines spatiotemporal variability in extreme precipitation across Paraná State, southern Brazil, based on observational, satellite‐derived and reanalysis datasets for 1983–2024. Extremes were quantified using indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) at annual, seasonal and monthly scales, with trend magnitude estimated using Sen's slope and statistical significance assessed at the 5% level. The results reveal pronounced structural dependence of detected trends on dataset type, affecting not only magnitude but frequently the sign and spatial coherence of change. Reanalysis products indicate comparatively homogeneous inland drying, with annual PRCPTOT reductions frequently exceeding −10 mm yr. −1 across central‐northern and western basins and significant CDD increases covering 40.7% of Paraná in ERA5, 57.4% in ERA5‐Land and 56.4% in AgERA5. By contrast, the observational reference retains stronger mesoscale heterogeneity, with significant annual CDD trends limited to 8.5% of the area and localised intensification of daily extremes in eastern basins subject to coastal and orographic influences. Monthly diagnostics identify a coherent seasonal shift, with April PRCPTOT declines reaching −4.48 mm yr. −1 in BR‐DWGD and −3.81 mm yr. −1 in ERA5, while September combines widespread PRCPTOT reductions with CDD increases up to 0.24 days yr. −1 . Conversely, October exhibits systematic precipitation increases, with PRCPTOT trends reaching 4.16 mm yr. −1 in BR‐DWGD, 4.18 mm yr. −1 in CHIRPS and 3.42 mm yr. −1 in PERSIANN‐CDR. This configuration suggests an extension of the mid‐year dry interval and a redistribution of rainfall toward the onset of austral spring. The findings demonstrate that assessments of extreme precipitation change in convectively dominated subtropical regions are strongly conditioned by dataset structure and temporal aggregation, highlighting the need to explicitly account for structural uncertainty in regional climate diagnostics and hydroclimatic risk evaluation.