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Soil respiration (Rs) plays an important role in the carbon (C) dynamics of terrestrial ecosystems and is strongly regulated by nitrogen (N) inputs. While the impact of N fertilization on Rs has been widely documented in conventional farmland ecosystems, its patterns and influencing factors in perennial tea plantation systems are still poorly understood. In the study, we conducted a 15-year field experiment in a representative tea plantation to investigate the effects of different N rates (0, 112.5, 225, and 450 kg N ha−1 yr−1) on Rs. Compared to the control (N0), soil pH decreased significantly (p < 0.05) by 6.07%, 11.82%, and 16.12% under N112.5, N225, and N450, respectively. Concurrently, cation exchange capacity (CEC), ammonium (NH4+-N), nitrate (NO3−-N), and available phosphorus (AP) increased with increasing N rates, whereas available potassium (AK) decreased. Soil microbial biomass carbon (MBC) initially increased and then decreased with increasing N rates, while dissolved organic carbon (DOC) content increased consistently. The Rs rate exhibited a distinct seasonal pattern with a single peak in August. The annual mean Rs rates were 2.79, 3.15, 4.06, and 3.85 μmol·m−2·s−1 for the N0, N112.5, N225, and N450 treatments, respectively. Soil temperature explained 55.41% to 61.08% of the variation in Rs rates across N treatments, and a composite model incorporating both soil temperature and moisture further improved the prediction of Rs dynamics. Cumulative soil CO2 emissions (CCEs) over the study period ranged from 10,427 to 14,221 kg CO2-C ha−1 across treatments and were significantly negatively correlated with soil pH, and positively correlated with DOC, MBC, and NO3−-N content. A non-linear relationship between N application rate and CCEs was observed, highlighting the complexity of optimizing N management for balancing productivity and climate mitigation in tea plantation systems. These findings provide a theoretical basis for developing rational N fertilization strategies and improving the predictive capacity of C cycle models in agroecosystems.