Radon (²²²Rn) dissolved in soil gas serves as a well-established geochemical tracer for crustal deformation and seismic precursory research. However, radon concentration time series recorded at near-surface monitoring stations are systematically contaminated by meteorological parameters, principally atmospheric temperature, humidity, barometric pressure, and precipitation and rigorous characterization of these influences is a prerequisite for reliable earthquake precursor signals. Conventional linear correlation analyses are fundamentally limited in their capacity to capture transient, event-driven, and nonstationary radon responses that are most likely to encode seismically significant precursory signals. This study presents a comprehensive <strong>nonlinear event-space analytical framework</strong> applied to 11,572 hourly soil-gas radon data recorded at the Jiaosi monitoring station in northeastern Taiwan spanning January 2021 to April 2022. The methodology integrates time-series first-order differencing to isolate event-based change signatures, classification of hike, drop, and normal meteorological change states, and Locally Weighted Scatterplot Smoothing (LOWESS) for nonparametric characterization of radon response curves in change space. In the linear amplitude domain, temperature (r = 0.500), atmospheric pressure (r = −0.384), and humidity (r = 0.158) emerge as the dominant meteorological correlates of radon variability. Critically, these correlations weaken markedly when evaluated in the nonlinear event domain, revealing that temperature, pressure, and humidity primarily modulate quasi-stationary and periodic background radon signals rather than transient excursions. In striking contrast, rainfall which exhibits negligible linear correlation with radon reveals a pronounced <strong>U-shaped nonlinear response</strong> in event space. The nonlinear rainfall response remained stable across all threshold configurations and accumulation windows, confirming the robustness of the event-space framework. in the event domain that is consistent across all precipitation accumulation windows (24-hour, 72-hour, and 240-hour), and is mechanistically attributable to the rainfall capping effect, piston effect, and soil drainage dynamics. This pattern constitutes the primary source of false positives in radon-based precursor identification. The event-space LOWESS framework substantially enhances the discriminability of meteorologically driven noise from potentially geophysically significant anomalies, providing a computationally accessible complement to Empirical Mode Decomposition–Hilbert-Huang Transform (EMD-HHT) approaches. The methodology has direct implications for operational earthquake precursor detection frameworks and multi-station radon monitoring networks.