Quantifying the synergistic interactions among water, heat, carbon, and nitrogen dynamics with crop growth in plastic-mulched maize systems is crucial for sustainable agriculture in arid areas. However, this remains a major gap in current modeling capabilities. Using multi-source field data (2019-2021) collected from drip irrigation under mulch (DM) and border irrigation under mulch (BM) in Northwest China, we employed the CoupModel to simulate these coupled processes. Sensitivity analysis identified 23 key sensitive parameters from 304 candidates. The model performed well in simulating soil water content, soil water storage, soil evaporation, plant transpiration, soil temperature, latent heat flux, sensible heat flux, gross primary production, net ecosystem exchange, ecosystem respiration, N2O flux and leaf area index. Over the following 20 years, DM maintained relatively stable interannual variations in evapotranspiration (ET) and irrigation water requirement (ET-P), while BM exhibited irregular ET fluctuations and increasing ET-P, suggesting greater water scarcity risks. Under both irrigation methods, photosynthesis and respiration of maize fields will decrease, while N2O emissions will increase relative to 2025 levels. In the extreme heat and drought year of 2093 compared with 2025, the DM showed larger relative reductions in ET (-17.25%) and gross primary production (-13.38%) than BM (-8.90% and -5.72%, respectively). In addition, water use efficiency of maize fields will improve, increasing by 4.59% under DM and 3.49% under BM relative to 2025. These results demonstrate the robustness and predictive capability of the CoupModel in simulating complex agroecosystem dynamics, thus providing a reliable tool for supporting agricultural management decisions.