Dynamic Prediction of Drought and Flood Catastrophes Under Climate Change: A Coupled GM(1,1)–Weibull–Bootstrap Model for Zhengzhou, China (1951–2024)

Drought and flood catastrophes threaten water security in the Yellow River Basin, yet prediction is hampered by sparse extreme event records. This study couples catastrophe series extraction via GM(1,1), Weibull extreme value modeling, and bootstrap uncertainty quantification, applying it to 74 years (1951–2024) of annual precipitation in Zhengzhou, China. Percentile thresholds of 410 mm (drought) and 864.6 mm (extreme precipitation) yield five drought and seven extreme precipitation catastrophe years. Both Weibull fits pass three goodness-of-fit tests, whereas the GM(1,1) benchmark fails for the flood series (mean error 21.5% vs. 3.31% for drought). The 0.9 quantile recurrence times (17.8 and 22.9 years) project the next drought catastrophe around 2031 (90% bootstrap window 2025–2033) and the next extreme precipitation catastrophe around 2044 (2034–2047); conditional residual life forecasts shift these by only 1–2 years (to 2033 and 2045, respectively). Two truncated record hindcasts (2013 drought; 2003 flood) fell within the predicted windows, and threshold perturbation and alternative distribution analyses support their robustness. Divergent shape parameters (k = 2.47 vs. 1.29) reveal accelerating drought but decelerating extreme precipitation hazard, providing probabilistic lead times for disaster risk management in data-scarce regions.