Supplementary material reports on geospatial analyses and rainfall data quality in Rio de Janeiro, suggesting better understanding of climate variations.
Overview: This document constitutes Supplementary Material I for the manuscript entitled "Evaluating Satellite and Reanalysis Rainfall Products for Extreme Events in Rio de Janeiro State, Brazil". It gathers detailed technical information and complementary geospatial analyses that underpin the discussions presented in the main body of the article, ensuring the transparency of the observational data and the robustness of the statistical results. Materials Included: The material includes Table S1, which presents the complete inventory of the 58 rainfall stations used, detailing World Meteorological Organization (WMO) codes, geographic coordinates, altitude, the responsible institution (ANA/SGB/CPRM), and the percentage of missing data. Additionally, the file contains an exhaustive set of supplementary figures (Figures S1 to S12), which illustrate spatial patterns of statistical performance (PBIAS, Correlation, RMSE) and climate trends for various extreme rainfall indices. (Note: I translated "Correção" as "Correlation", assuming it was a typo for "Correlação", which is the standard metric alongside PBIAS and RMSE). Scientific Production Highlight: A highlight of this material is the detailed characterization of the sub-regions of the state of Rio de Janeiro and the spatialization of long-term trends (1983–2024). The results presented in Figures S1 to S12 allow for the observation of seasonal and annual variations in critical indices such as RX1Day, RX5Day, R95p, CDD, and CWD, providing a granular view of the agreement between satellite/reanalysis products and the surface observational network. Technical Notes: All station metadata were rigorously verified for spatial and temporal consistency. The supplementary figures follow the same automated processing protocols described in the code scripts associated with the study, ensuring the standardization and reproducibility of the cartographic and statistical results.
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Terassi et al. (2026) studied this question.
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