Process-based models help disentangle management effects from climate, soil and genetics influences on crop growth and development. However, model parameter sensitivity varies under different environmental and management conditions, posing challenges for model application. We conducted a global sensitivity analysis to identify key parameters of STICS model influencing winter wheat growth and yield under diverse nitrogen and water stress scenarios in the Huanghuaihai Farming Region (HFR) of China. HFR is China’s largest winter wheat planting region that contributes about 13 % of global wheat production. Our results revealed that parameters such as nitrogen critical dilution curve (bdil and adil) and leaf lifespan (durvieF) are highly sensitive to nitrogen stress. Similarly, the coefficient for water requirements (kmax) critically affects the responses of winter wheat to water stress. These parameters should therefore be calibrated under their respective stress conditions. Maximum temperature strongly influenced the sensitivity of tmaxremp, while precipitation shaped the model’s response to water stress. Additionally, soil properties (e.g., finert, pH and HMINF) played critical roles in mediating nitrogen-water stress effects. Parameter sensitivity varied across growth stages, for example, stlevamf exhibited high sensitivity (sensitivity index achieved 0.4) during jointing but showed negligible effects at other stages. After calibration and validation, STICS effectively simulated winter wheat under various nitrogen and water management with validation set rRMSE of 21 %, 8 % and 10 % for LAI, biomass and yield, respectively. These findings provide critical insights for improving STICS model accuracy in simulating winter wheat under various nitrogen-water management in the HFR of China, similar methods could be used in many other agroecological regions.