Abstract: Aiming at the demand for fine accounting of additional non-point source pollution in the intensive agricultural areas in the middle reaches of the Yellow River, this study takes the Weihe River Basin as a case study. By integrating land use data, rural population and livestock farming data were spatially allocated, enabling the disaggregation of county-level statistical data to a 1 km × 1 km grid scale. Coupled with emission coefficients and differentiated removal rates, the study achieved high-resolution estimations of two major categories of additional non-point source pollutants—total nitrogen (TN) and total phosphorus (TP)—from rural domestic sewage and livestock farming over the period 2000-2022. The results show that: (1) High-emission areas of TN and TP from rural domestic sewage were primarily located in the southeastern part of the basin, whereas those from livestock farming were mainly concentrated in the western region. Areas with expanding rural settlements and intensive livestock farming clusters emerged as pollution hotspots. (2) The information entropy (H) of TN and TP at the grid scale was higher than that at the county scale for both pollution sources, indicating that the grid scale better captures the spatial heterogeneity of pollutant distribution. For example, the information entropy of TN from rural domestic sewage at the grid scale was 13.76 in 2022, significantly higher than 5.15 at the county scale. Similarly, for livestock farming, the grid-scale H_TN reached 14.42, compared to 5.61 at the county scale. (3) In 2022, the hotspot areas identified at both grid and county scales exhibited a high degree of spatial overlap for both pollution sources. Over 70% of the total hotspot area was captured at the grid scale, which was substantially higher than the range of 32.6%-44.5% at the county scale. This suggests that the grid scale offers greater advantages in revealing spatial details and identifying zones of high pollutant concentration. Therefore, the downscaling accounting method proposed in this study significantly enhances the ability to characterize the spatial heterogeneity of additional non-point source pollutant emissions. The results of this study can provide scientific basis and data support for the efficient coupling of explicit pollution control and localized water quality response mechanism modeling in intensive agricultural areas in the middle reaches of the Yellow River.