A ranking–correction–fusion framework (HARMONY) for climate-zone-aware multi-source daily precipitation over China at 0.1°

Precipitation data is critical for hydrological modelling and for assessing floods and droughts. However, China's complex topography and pronounced climatic variations mean that data from a single source often exhibits significant regional and seasonal biases, making it challenging to produce stable, reliable nationwide products. This study presents a multi-source fusion framework-HARMONY for generating nationwide daily precipitation data. The core methodology involves objective selection, bias correction, regional adaptive fusion and hierarchical evaluation and interpretation. The structure initially picks out additional input datasets from possible products within the limits of various indicators for diagnosis. It then applies consistency bias control to reduce systematic error propagation, performing fusion at climate zoning scales to generate seamless national results. Multi-level evaluations incorporate continuous statistics, event detection and extreme indicators.