**5. Conclusions**

This study conducted the threshold recognition based on non-stationarity of extreme rainfall in the MLRYRB. The main conclusions can be summarized as follows:

First, five sub-regions with similar rainfall characteristics were identified. Using the BGSA method to detect the non-stationarity of extreme rainfall events under different thresholds in each region, it is found that the non-stationarity of extreme rainfall events in the MLRYRB would change with the selection of threshold. When the threshold was selected as 40–60 mm, the non-stationarity of extreme rainfall was similar to the non-stationarity of RX1day series, indicating that 40–60 mm should be a reasonable threshold interval of extreme rainfall.

Second, this study performed distribution fitting on the extreme rainfall screened by different thresholds. The fitting of the General Pareto Distribution (GPD) was much better than that of the Generalized Extreme Value Models (GEV) regardless of the threshold selection. Then, the GPD with different design return periods was calculated, and the uncertainty of the threshold in Sub-region V was slightly larger than those in the other four sub-regions. Combined with the uncertainty of the threshold in each subregion, the extreme rainfall thresholds in the five sub-regions were 45, 60, 50, 45, and 60 mm, respectively. Moreover, the extreme rainfall thresholds of the sub-regions close to the coast were higher than those far away from the coast.

Third, this study investigated the correlations of extreme rainfall with large-scale climatic patterns and found significant correlations between extreme rainfall and ENSO/WPSH. It is worth noting that WPSH is a large-scale climatic pattern that affects the rainfall in the entire YRB, and the impact of WPSH on the heavy rainfall is significantly stronger than that of ENSO. This suggests that accurate outputs from large-scale climate models can help to improve the extreme rainfall predictions in the MLRYRB.
