Urban Rainfall Island Detection Remains Uncertain: A Global Sensitivity Analysis Across Data Sets and Methods

Abstract Urbanization can enhance local precipitation through thermodynamic and dynamic effects, yet detection of the urban rainfall island (URI) effect remains uncertain at the global scale because results vary across data sets, methods, and spatiotemporal resolutions. Here, we systematically assess the sensitivity of URI detection across 741 global cities using four precipitation products, multiple metrics, different city boundaries, and varying temporal and spatial scales. URI occurrence exhibits pronounced spatial heterogeneity and substantial dependence on precipitation data and metrics, with limited urban definition effects. Nevertheless, several features are consistent including winter maxima, morning and evening peaks, and weakening spatial gradients away from city centers. URI signals are also more prominent in larger cities and in tropical climates, highlighting functional dependencies on urban and climatic attributes. These results provide the first global benchmark of URI detectability across data sets, methods, and scales, identifying both robust features and major uncertainties, offering practical guidance for future URI analysis.