Traditional statistical approaches for extreme precipitation projections suffer from systematic biases in direct climate model precipitation outputs. This study develops an innovative covariate-based max-stable process (MSP) framework that leverages physics-informed climate variables rather than biased precipitation projections to analyze extreme precipitation across Bangladesh. Systematic screening of 2500+ model combinations across 35 meteorological stations, five seasons, and ten durations (1–48 h) identified optimal MSP characterizations with seasonal specificity: Brown-Resnick processes dominate the SW-Monsoon while Extremal-t Bessel characterizations optimize Winter extremes. Geographic variables (latitude, coastal proximity, and elevation) systematically outperform large-scale climate teleconnections across all conditions, indicating that Bangladesh's extreme precipitation is controlled by regional Bay of Bengal dynamics rather than remote ocean oscillations. Seasonal intensity–duration–frequency (IDF) analysis establishes SW-Monsoon extremes reaching 37–64 mm h⁻¹ for 25-year, 1-h events, with distinct seasonal hierarchies reflecting a monsoon-dominated climate. Climate change projections under the SSP2-4.5 scenario (2025–2050) reveal substantial spatial heterogeneity (−57% to +79%), with differential responses across the probability spectrum: rare 100-year events intensify by +37% while frequent 2-year events decrease by −51%, indicating tail-stretching of extreme distributions. The results highlight that infrastructure design standards require distinct treatment of rare versus frequent extremes, with implications for climate adaptation in monsoon-dominated regions worldwide.
Nikmah et al. (Wed,) studied this question.