Predictability and skill of convection-permitting ensemble forecast systems in predicting the record-breaking “21·7” extreme rainfall event in Henan Province, China

During 19–21 July 2021, an extreme rainfall event occurred in Henan Province, China, during which a record-breaking maximum hourly rainfall of 201.9 mm was recorded in Zhengzhou at 09 UTC July 20. In this study, the predictability of this extreme rainfall event is investigated using two convection-permitting ensemble forecast systems (CEFSs): one initialized from NCEP GEFS (named CEFS_GEFS) and the other initialized from time-lagged ERA5 data (named CEFS_ERA). Both are able to reproduce the daily heavy rainfall along the Taihang Mountains, but most members have significant position biases for the extreme rainfall in Zhengzhou. For the hourly rainfall, a few members are able to capture the evolution and propagation of extreme rainfall. However, all ensemble members underestimate the extreme hourly rainfall and have position errors of a few tens to a few hundreds of kilometers. Such results suggest that the predictability of the extreme hourly rainfall at the accuracy of city scale in Zhengzhou is low, especially by deterministic forecasting models, and the occurrence of the extreme requires that many favorable conditions to happen simultaneously. In terms of the Brier score, CEFS_GEFS performs better than CEFS_ERA. The latter lacks spread, especially in regions with scarce rain, resulting in less dispersion in precipitation distributions and larger probability forecast error. When a neighborhood is applied, the probability of precipitation (POP) is significantly increased over Zhengzhou. While the traditional POP shows almost no skill for hourly rainfall ≥ 25 mm h−1, the neighborhood POP significantly improves the forecast skill score, for both daily and hourly rainfall, suggesting higher predictability when spatial error among the ensemble members is allowed.