Abstract: Winter wheat is one of the most important staple crops in the North China Plain. With intensifying challenges of climate change and water shortage, the need for sustainable and high efficiency winter wheat production is becoming more pressing. Clear understanding of the control factors of wheat production can provide the necessary basis for the regulation of a smart winter wheat production policy. Hebei Province is one of the major winter wheat production regions in North China. Annual records (2004–2013) of wheat yield and agricultural information for each county in the province were used to analyze the spatial distribution of yield variation of winter wheat along with the control factors. The major winter wheat production area (which consists of 101 counties with winter wheat area greater than 20% of total cropping area of the county) in Hebei Province was selected and divided into 4 zones (Ⅰ–Ⅳ) using hierarchical clustering analysis based on maximum, minimum and average yields. A principal component analysis was conducted using the average and variation indexes (minimum, maximum, bottom and top boundaries of the 95% confidence interval) of the agricultural variables for winter wheat production, including irrigated area, fertilizer and pesticide amount as well as annual rainfall in each county. Five derived principal factors represented fertilizer (F₁), pesticide (F₂), irrigation (F₃), annual rainfall (F₄) and minimum rainfall (F₅). Based on the factor score values of the factor models, a stepwise regression analysis model was developed to assess the spatial variation of winter wheat yield as an independent factor (Y). The results showed that from zone Ⅰ to zone Ⅳ, averaged winter wheat yield decreased, whereby temporal yield variation increased significantly (P < 0.05). Zone Ⅰ mostly covered central Hebei Plain including Gaocheng and Luancheng Counties to the east of Shijiazhuang City. Zones Ⅱ, Ⅲ and Ⅳ diverged from the central to the peripheral of Hebei Plain in that sequence, especially zone Ⅳ which only covered the southwest and northeast corner with the lowest yield and the highest variation. Model analysis showed that Y was significantly correlated with F₁, F₃, F₄ and F₅ (R² = 0.685, P < 0.01) and these factors explained 68.5% of Y, where irrigation (F₃) was the most important factor explaining 37.4% and fertilizer (F₁) explaining 21.7%. Annual rainfall F₄ and F₅ together explained only 9.4% of the whole model result, indicating a minor effect of annual rainfall on the spatial distribution of winter wheat production in Hebei Province. Pesticide (F₂) was not factored into the Y regression model, showing that the use of pesticide had no significant effect on winter wheat production. It underlined the point that plant diseases and pests happened at random and with no regional tendency in winter wheat production. Regression analysis on winter wheat yield and the ratio of winter wheat area to total cropped area (F₆) showed that F₆ coincidently varied with winter wheat yield among different counties (R² = 0.409, P < 0.05). Only a few counties to the far east of the low plain close to the coast belonging to zone Ⅳ with the lowest winter wheat yield but abnormally high F6. The results showed that winter wheat production was basically more proper under the existing management mode. However, under severe water shortage conditions, the abnormally high F6 value with low yield spots needed careful examination to insure a healthy and sustainable high-efficiency cropping system. Otherwise winter wheat production should be reduced in low yield areas because irrigation was the most important controlling factor of production. Nevertheless, in this study, only 68.5% of the production variables were explored. This implied that there were other factors not only taken into account in the analysis but also affected winter wheat production.