Research on the indirect value of rice production has primarily focused on ecosystem service functions, with less emphasis on externalities. This article conducts an innovative evaluation of the externality value of rice production and its significant impact on the ecological transformation of grain production. Utilizing the Chinese panel data on rice production from 2000 to 2021, the study applies methods such as the functional value approach, hotspot analysis, and spatial autocorrelation, in conjunction with geodetectors and geographically weighted regression, to assess the externality value of rice production. It further analyzes the spatiotemporal evolution and driving factors of these externalities. Findings include: (1) the annual average positive, negative, and net externality values of Chinese rice production are 603.067 billion, 208.027 billion, and 395.048 billion yuan, respectively. The composition of positive externality values includes primarily water conservation (34.84%) and gas regulation (23.41%), whereas greenhouse gas emissions (35.93%) and fertilizer pollution (20.71%) constitute the primary components of the negative externality values. (2) The overall trend for positive externality value indicates a slow growth, with increase of 8.78%; post-2012, marked as the beginning of a new era, the negative externality value shows a trend of initial increase followed by a decrease, demonstrating the effectiveness of ecological prevention and control in this new era. (3) The strongly spatial heterogeneity is evident, with the concentration of externality values predominantly in areas east of the Hu Line. In the rice-producing regions, the annual average net values in Central China, South China, Southwest China, Northeast China, and North China are 174.484 billion, 57.324 billion, 73.947 billion, 49.651 billion, and 33.765 billion yuan, respectively. Externality values demonstrate a positive global correlation with geographic space, and the overall spatial clustering effect is weakened. In Central and South China, hotspot areas and high-high clustering predominate, while in North China and Northwest China, coldspot areas, low-low clustering, and non-significance are more prevalent. (4) Externalities are influenced by the coordinated interaction of natural and socio-economic factors, exhibiting a dual-factor enhancement trend. The significant impacts arise from total mechanical power and variable agricultural inputs. Spatial heterogeneity in the driving forces of externalities is evident across different regions, with social factors exert a greater influence in the southern areas compared to the northern ones.