Using the tropical cyclone (TC) rainfall database from the Yearbook of Tropical Cyclones as the verification benchmark , this study combines of 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 accumulations (TPA) forecasts . The assessment covers nine TCs that affected mainland China in 2022, with forecasts generated by global and regional models , an ensemble - mean method , and the CMA Intelligent Grid Forecast System (CMA-NDFS). Results show that the Threat Scores (TS) for both TC daily and TPA forecasts decreased with lead times and precipitation intensities . The average TS of the seven forecast methods for TC daily heavy rain (exceeding 50 mm of daily accumulated precipitation) was 0.27, and 0.18 for extreme heavy rain (exceeding 100 mm of daily accumulated precipitation) . Using a TS score above 0.10 as the benchmark for a practical forecast , CMA - NDFS can provide skillful forecasting of TC daily rainfall with lead times of up to 120 h. The average TS scores for TPA forecasts at each rainfall threshold were: 0.36 (≥ 10 mm) , 0.23 (≥ 25 mm ) , 0.16 (≥ 50 mm) , 0.09 (≥ 100 mm) , and 0.01 (≥ 250 mm) . Among these, CMA - NDFS achieved the highest TS scores at all precipitation thresholds in TC TPA forecast. Regarding TC TPA errors, Forecasting methods generally overestimated the TPA for summer TCs and underestimated them for autumn TCs. MODE analysis revealed that the main source of systematic errors lied in underestimations of precipitation area, as the precipitation threshold increases , the total interest decreases, and the probability of large angle differences increases . Westward and northwestward moving TCs are better predicted than right-recurving TCs . Although the number of cases analyzed in this study is limited, the verification results still reflect the current forecasting performance for TC precipitation and indicate potential sources of systematic errors.
 

tropical cyclone,total precipitation accumulations,precipitation verification,spatial evaluation,forecast error