Zhang Yinchi, Deng Chao, Xu Wanling, Zhuang Yao, Jiang Lizhi, Jiang Caiying, Guan Xiaojun, Wei Jianhui, Ma Miaomiao, Chen Ying, Peng Jian, Gao Lu
Key Laboratory for Humid Subtropical Eco-geographical Processes of the Ministry of Education, Fujian Normal University, Fuzhou, 350117, China.
Institute of Geography, Fujian Normal University, Fuzhou, 350117, China.
Sci Rep. 2025 Jan 2;15(1):156. doi: 10.1038/s41598-024-84076-x.
Global warming has profound effects on precipitation patterns, leading to more frequent and extreme precipitation events over the world. These changes pose significant challenges to the sustainable development of socio-economic and ecological environments. This study evaluated the performance of the new generation of the mesoscale Weather Research and Forecasting (WRF) model in simulating long-term extreme precipitation events over the Minjiang River Basin (MRB) of China from 1981 to 2020. We calculated 12 extreme precipitation indices from the WRF simulations and compared them with observations. The spatio-temporal variations of extreme precipitation were further analyzed in terms of intensity, frequency, and duration. The results indicated that the WRF model can appropriately reproduce the spatial distribution of extreme precipitation indices with acceptable biases. The performance is significantly better for intensity and frequency indices compared to duration indices. Except for PRCPTOT and R10mm, WRF accurately captures the interannual variations of extreme precipitation. Meanwhile, the results of the pre-whitening Mann-Kendall (PWMK) test suggested that WRF can identify significant increasing trends in extreme precipitation, particularly for R95p, R99p, and R50mm. This study provides valuable insights for extreme precipitation forecasting and warning in other mountainous regions.
全球变暖对降水模式有着深远影响，导致全球范围内降水事件更加频繁且极端。这些变化给社会经济和生态环境的可持续发展带来了重大挑战。本研究评估了新一代中尺度天气研究与预报（WRF）模型在中国岷江流域（MRB）模拟1981年至2020年长期极端降水事件的性能。我们从WRF模拟中计算了12个极端降水指数，并将它们与观测值进行比较。从强度、频率和持续时间方面进一步分析了极端降水的时空变化。结果表明，WRF模型能够以可接受的偏差适当再现极端降水指数的空间分布。与持续时间指数相比，强度和频率指数的性能明显更好。除PRCPTOT和R10mm外，WRF准确捕捉了极端降水的年际变化。同时，预白化曼-肯德尔（PWMK）检验结果表明，WRF能够识别极端降水中的显著增加趋势，特别是对于R95p、R99p和R50mm。本研究为其他山区的极端降水预报和预警提供了有价值的见解。
