To address the limitations in existing urinary stone recurrence (USR) models, including failure to account for changes in 24-hour urine (24U) parameters over time and ignoring multiplicity of stone recurrences, we presented a novel statistical method to jointly model temporal trends in 24U parameters and multiple recurrent stone events. The MSTONE database spanning May 2001 to April 2015 was analyzed. A joint recurrent model was employed, combining a linear mixed-effects model for longitudinal 24U parameters and a recurrent event model with a dynamic first-order Autoregressive (AR(1)) structure. A mixture cure component was included to handle patient heterogeneity. Comparisons were made with existing methods, multivariable Cox regression and conditional Prentice-Williams-Peterson regression, both applied to established nomograms. Among 396 patients (median follow-up of 2.93 years; IQR, 1.53-4.36 years), 34.6% remained free of stone recurrence throughout the study period, 30.0% experienced a single recurrence, and 35.4% had multiple recurrences. The joint recurrent model with a mixture cure component identified significant associations between 24U parameters - including urine pH (adjusted HR = 1.991; 95% CI 1.490-2.660; p < 0.001), total volume (adjusted HR = 0.700; 95% CI 0.501-0.977; p = 0.036), potassium (adjusted HR = 0.983; 95% CI 0.974-0.991; p < 0.001), uric acid (adjusted HR = 1.528; 95% CI 1.105-2.113, p = 0.010), calcium (adjusted HR = 1.164; 95% CI 1.052-1.289; p = 0.003), and citrate (adjusted HR = 0.796; 95% CI 0.706-0.897; p < 0.001), and USR, achieving better predictive performance compared to existing methods. 24U parameters play an important role in prevention of USR, and therefore, patients with a history of stones are recommended to closely monitor for future recurrence by regularly conducting 24U tests.