Forecasts Verification on Tropical Cyclone Precipitation Influencing China in 2022

Using the TC precipitation database from the Yearbook of Tropical Cyclones (TCs) as the reference dataset, this study combines traditional statistical verification, error analysis, and the Object-Based Diagnostic Evaluation (MODE) spatial verification method to evaluate the performance of TC daily precipitation and total precipitation accumulation(TPA) forecasts. The evaluation covers nine TCs that affected mainland China in 2022, with forecasts from global and regional numerical weather prediction models, an ensemble-mean method, and the CMA Intelligent Grid Forecast System (CMA-NDFS). For TC daily precipitation, the seven forecast methods yielded average TS of 0.27 for heavy rain (daily precipitation ≥50 mm) and 0.18 for extreme heavy rain (daily precipitation ≥100 mm). Using a TS of 0.10 as the benchmark for a practically useful forecast, CMA-NDFS provided skillful TC daily precipitation forecasts with lead times of up to 120 h during the 2022 evaluation period. For TPA forecasts, the average TS across the seven methods were 0.36 (≥10 mm), 0.23(≥25 mm), 0.16(≥50 mm), 0.09(≥100 mm), and 0.01(≥250 mm), respectively. Among these, CMA-NDFS achieved the highest TS at most precipitation thresholds across the nine cases. The MODE method analysis revealed that the main source of systematic errors lied in underestimations of precipitation area for TPA forecasts. In addition, as the precipitation threshold increased, the total interest decreased and the likelihood of large angular differences increased.Forecast performance also varied with TC track: westward- and northwestward-moving TCs were generally better predicted than right-recurving TCs. Because the analysis is based on only nine cases from a single La Niña year, the results may be influenced by year-specific circulation anomalies. Further evaluation using a larger and more diverse sample of TCs is needed to consolidate and generalize these findings.