Spatiotemporal Patterns of 45-Day Precipitation in Rio Grande do Sul State, Brazil: Implications for Adaptation to Climate Variation

Abstract

Understanding precipitation variability is essential for assessing climate dynamics and their impacts on agriculture, water resources, and infrastructure. This study analyzes subseasonal precipitation patterns in Rio Grande do Sul, Brazil, using 45-day accumulated intervals over a 17-year period (2006–2022), a timescale critical for understanding drivers of extreme events like the catastrophic floods of 2024. A total of 138 precipitation fields were generated from 670 spatial points. Spatial analysis revealed median precipitation values ranging from 130 to 329 mm/45 days, with the northeast showing the highest accumulations and the southwest showing the driest conditions. Temporal variability was marked by abrupt anomalies, with median peaks up to 462 mm and minima of 33 mm. Significant temporal autocorrelation (lag-1, 45 days) was identified in the central and northern regions, while lag-2 (90 days) showed inverse patterns in the south (correlation coefficient ≈ −0.45). Principal component analysis (KMO = 0.909; Bartlett’s χ2 = 187,990.945; p < 0.05) identified seven dominant modes, with PC1 explaining 26% of total variance and highlighting extremely wet anomalies (e.g., SPI > 2.0). Correlation with the Oceanic Niño Index revealed heterogeneous responses to ENSO phases, with strong El Niño episodes (2009, 2015–2016) associated with precipitation peaks up to 966 mm/45 days. These results underscore the importance of subseasonal scales for understanding climate anomalies and support the development of regional forecast strategies and water management policies under increasing climate variability.