Extreme Rainfall Analysis in Pernambuco, Northeast Brazil, Using a High‐Resolution Gridded Dataset

ABSTRACT This paper presents a detailed spatio‐temporal analysis of the rainfall in the state of Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by the Expert Team on Climate Change Detection, Monitoring and Indices. To accomplish this, daily rainfall 1data (1961–2019) were extracted from 809 high‐resolution grid points (0.1° × 0.1°) using the Brazilian Daily Weather Gridded Data (BR‐DWGD). The significance and magnitude of index trends were assessed using the modified Mann–Kendall and Sen's slope tests. This study also examined whether there existed a significant difference in climate indices among the three regions (Sertão, Agreste and Zona da Mata) within the state. The findings revealed notable significant negative trends in the PRCPTOT, R10mm, R20mm, Rx1day, Rx5day and CWD indices across all regions of Pernambuco, exhibiting a gradient from the coast to the state's interior. Reduction values of up to 15 mm year −1 for PRCPTOT, 0.7 day year −1 for R10mm, 0.2 day year −1 for R20mm, 0.01 mm year −1 for Rx1day, 0.03 mm year −1 for Rx5day, 0.4 day year −1 for CWD were observed. Furthermore, an alarming pattern was also noted for CDD, displaying a higher concentration of significant positive trends in all regions of the state, with estimated increases of up to 1.4 day year −1 . Conversely, a balance of trends—both positive and negative—was observed across the entire state for R95p and R99p, with a majority of trends proving non‐significant. SDII exhibited a higher frequency of grid points showing a significant positive trend, particularly notable in the Sertão and Zona da Mata regions, where significant differences in the index values were absent. However, the remaining indices showcased notable regional differences, with values decreasing from the east to the west of the state, except for CDD. This study will assist decision makers, providing detailed long‐term information essential for preventing natural disasters and supporting socioeconomic and environmental policies in the state.