Large-Scale Climate Drivers of Spatially and Temporally Compounding Hydroclimatic Extremes

The El Niño–Southern Oscillation (ENSO) is a dominant source of interannual climate variability, strongly influencing hydroclimatic extremes across the U.S. Great Plains (USGP). This study examines the seasonal and lagged impacts of ENSO phases—El Niño, La Niña, and Neutral—on precipitation-based extremes over the USGP for the period 1950–2023. ENSO phases were identified using the Oceanic Niño Index (ONI) with ±0.5 °C thresholds, and seasonal transitions (DJF, MAM, JJA, SON) were analyzed to characterize persistent, isolated, and whiplash ENSO extremes. High-resolution precipitation datasets from PRISM and NOAA Climate Divisions were integrated within a GIS framework to develop seasonal time series and conduct spatial analyses at the climate-division scale. Composite anomaly maps of precipitation percentiles were generated and spatially aggregated using zonal statistics, while Pearson and Spearman correlation analyses, including 3–12-month lags, quantified delayed and region-specific ENSO responses. Statistical significance of phase-wise differences was evaluated using ANOVA, Kruskal–Wallis, and Mann–Whitney U-tests. Results reveal pronounced seasonal asymmetry in ENSO impacts, with La Niña strongly associated with drought conditions in the southern plains and El Niño linked to enhanced wet anomalies across central and eastern regions. The identification of ENSO-sensitive zones improves regional climate predictability and provides actionable insights for anticipatory water-resources management. Overall, the study demonstrates the effectiveness of integrating geospatial analysis, long-term climatological datasets, and robust statistical methods to attribute hydroclimatic extremes to large-scale ocean–atmosphere variability.