Abstract:Track forecasting is the greatest progress over past decades in numerical weather prediction (NWP) regarding the tropical cyclones (TCs). But the landfalling TC precipitation prediction capability of NWP models is still very limited. Focusing on combining dynamical and statistical methods, a new method named track similarity based Landfalling Tropical cyclone Precipitation (LTP) Dynamical Statistical Ensemble Forecast (LTP_DSEF) model has been developed. The LTP_DSEF model includes five steps: TC track prediction, identification of analogue TC tracks, identification of other analogue TC characteristics, ensemble forecasting of TC precipitation, and the best scheme selection. There are two key technologies involved: the Objective Synoptic Analysis Technique (OSAT) for partitioning TC precipitation and the objective TC Track Similarity Area Index (TSAI). The application of LTP_DSEF model for 21 TCs with accumulated LTP ≥100 mm in South China during 2012-2016 reveals that the new method shows a better performance than the three NWP models (EC, GFS and T639) in predicting LTP. For ≥50 mm LTP forecasts, the threat score (TS) of LTP_DSEF models results are superior to those of the three NWP models. Analysis of the values of the LTP_DSEF models parameters for three best schemes shows that, the initial time designed to be the closest to the impact time, i.e., 1200 UTC on the previous day with precipitation on land and the ensemble parameter being the maximum are both beneficial to ideal forecasts.