A Hybrid Machine Learning–Process-Based Modelling Approach to Explore Dynamic Responses and Spatial Heterogeneity of N₂O Emissions in Rice Paddies

• Nitrous oxide (N₂O) emissions from rice paddies represent an important but highly variable pathway of nitrogen loss, with strong dependence on local hydrological conditions, soil properties, climate regimes and management practices. This pronounced variability poses major challenges for process-based models (PBMs), which often rely on fixed functional structures and site-specific parameterization, limiting their ability to generalize across heterogeneous regions. In this study, we develop a hybrid modelling framework that integrates machine learning (ML) with PBMs to improve predictive generalization while retaining mechanistic interpretability. Within this framework, PBMs are used to explicitly describe N₂O responses to key environmental drivers, whereas the ML component is employed to capture, distill and generalize data-driven response relationships from multi-site observational datasets compiled at the global scale. Beyond methodological development, the hybrid approach is used to explore the spatial heterogeneity and dynamic responses of N₂O emissions across contrasting rice-growing regions. By jointly analysing climatic, soil and management drivers, we assess how response behaviours may differ between regions and under varying water management regimes. Our results highlight the potential of hybrid modelling as both a predictive and diagnostic tool for understanding N₂O variability in rice paddy systems. This framework provides a flexible foundation for future scenario analysis and supports the development of region-specific mitigation strategies for more sustainable rice production.