A region-adaptive framework for design precipitation estimation: case study for the Chinese mainland

In recent decades, increasingly severe precipitation-driven flood events have been observed across many regions, posing substantial challenges to infrastructure safety and risk management. Reliable estimation of design precipitation associated with rare events is essential for improving flood preparedness and engineering design. In the Chinese mainland, the Pearson Type III (PE3) distribution has been recommended as a unified model for estimating precipitation extremes since 2006. However, this assumption neglects pronounced spatial heterogeneity and may lead to biased estimates of design precipitation, affecting engineering design and risk assessment. In this study, we evaluate 7 commonly used extreme-value distributions for estimating century-scale precipitation extremes in the Chinese mainland, using high-resolution gridded precipitation data from 1961 to 2022. Our results create a composite Regionally Optimal Distribution framework considering 3 algorithms (ROD-3) involving Generalized Extreme Value (GEV), Weibull (WEI), and PE3. The ROD-3 framework provides the optimal fit for over 92% of grids in the Chinese mainland, with GEV best for 54%, followed by WEI and PE3. Compared to PE3, ROD-3 significantly improves goodness-of-fit in 65% of regions and reduces estimation bias for the 100-year return period design precipitation in 22% of regions. In addition, a decision-tree classifier identified a kurtosis threshold (∼0.71) as a simple criterion for distribution selection. Further validation using global land precipitation data reveals a consistent threshold, supporting the framework's transferability across diverse climatic regions. Overall, the framework provides an operational tool for region-specific design standards and risk-informed infrastructure planning, improving the reliability of design precipitation estimation.