Performance evaluation of multi‐precipitation products fusion based on a novel dynamic K‐nearest neighbour Bayesian ensemble calibration framework

The fusion of multiple precipitation products can effectively improve precipitation accuracy. In order to reduce the uncertainty of traditional precipitation members and improve the applicability of Bayesian model averaging (BMA), this study proposes a dynamic ensemble calibration framework with seasonality and real‐time capability, namely dynamic K‐nearest neighbour BMA (DKBMA), for the integration calibration and comparison of ERA5 reanalysis products and three satellite precipitation products (CMORPH, 3B42RT and 3B42V7). The application results of the DKBMA in the Yujiang River basin, Southern China, during the period of 2011–2016 show that (1) the DKBMA can overcome the problem of ERA5 error interference on the seasonal scale, reduce the systematic bias of ensemble precipitation members at coastal sites and demonstrate strong robustness at different altitudes. (2) Compared with the traditional BMA, the DKBMA has significantly improved accuracy, especially in capturing extreme precipitation events. The correlation coefficient has increased from 0.793 (traditional BMA) to 0.841 (DKBMA), the root‐mean‐square error has decreased from 35.61 (traditional BMA) to 30.95 (DKBMA), and the absolute value of relative bias has decreased from 62.80% (traditional BMA) to 49.94% (DKBMA). The proposed DKBMA in this study can provide a new solution for the fusion of multi‐source precipitation products in data‐scarce regions. The novel DKBMA considers the influences of seasonal climate, terrain factors, time scales and elevations on precipitation product fusion, providing a new solution for the fusion of multi‐source precipitation products in data‐scarce regions. Compared with the traditional BMA, the DKBMA has significantly improved accuracy, especially in capturing extreme precipitation events.