A hybrid approach for regionalization of precipitation based on maximal discrete wavelet transform and growing neural gas network clustering.

Understanding the spatiotemporal variability of precipitation is critical for effective water resource planning, particularly in regions with diverse climatic zones such as China. This study presents a hybrid methodology combining the Maximal Overlap Discrete Wavelet Transform (MODWT) and the Growing Neural Gas (GNG) clustering algorithm to regionalize precipitation patterns using monthly data from 123 synoptic stations over a 45-year period (1980-2024). MODWT was applied to decompose the precipitation time series into five frequency-based sub-series (W1-W5 and V5), capturing variability across 2- to 32-month cycles. Shannon entropy was calculated for each sub-series, generating a comprehensive feature set that reflects the temporal complexity at each station. These entropy features were subsequently used as input for the GNG algorithm, which identified 12 homogeneous precipitation clusters. The clustering performance was quantitatively assessed using the silhouette coefficient (SC), where the proposed model achieved a maximum SC value of 0.68, indicating strong inter-cluster separation and intra-cluster compactness. In comparison, clustering performed without MODWT-based preprocessing yielded a lower SC value of 0.56, highlighting the effectiveness of the hybrid approach. Spatial analysis revealed that northern and northwestern China exhibited the highest precipitation variability, particularly in the W3 (8-month) and V5 (trend) components, while southern and southeastern regions demonstrated more stable patterns. The results underscore the value of integrating multiscale temporal analysis with neural-based clustering for robust and interpretable regionalization of precipitation. This framework offers substantial potential for informing water resource management, climate adaptation policies, and infrastructure development under future hydroclimatic uncertainty.