Background: High-resolution meteorological exposure assessment is essential for individual-level environmental epidemiology. However, clear methodological guidance on the optimal spatial interpolation technique for daily meteorological variables at the national scale remains limited. Methods: Using daily observations from 2417 national meteorological stations across mainland China from 2010 to 2021, we systematically benchmarked 2 widely used spatial interpolation methods—Inverse Distance Weighting (IDW) and Ordinary Kriging (OK). Twelve representative days capturing seasonal variability were selected, and 10-fold cross-validation was conducted. Interpolation performance was evaluated using root mean squared error (RMSE), standardized mean absolute percentage error (sMAPE), Nash–Sutcliffe efficiency (NSE), bias, and computation time. Results: Across 12 representative days from 2010 to 2021, IDW consistently outperformed OK in national-scale 10-fold cross-validation. For daily mean temperature, IDW achieved lower prediction errors, with RMSE ranging from 1.52°C to 1.75°C, compared with 1.50°C to 1.81°C for OK, and consistently higher NSE values (0.83-0.97 vs 0.82-0.97). Similar performance advantages were observed for relative humidity. Bias estimates were close to zero for both methods, indicating minimal systematic error. In addition, IDW showed modest computational advantages, with average processing times of approximately 96 s/day, compared with approximately 99 s/day for OK, supporting its suitability for large-scale meteorological exposure reconstruction in epidemiological studies. Conclusions: From an epidemiological exposure assessment perspective, IDW provides a favorable balance between accuracy, computational efficiency, and preservation of spatial variability. These findings offer practical methodological guidance for large-scale individual-level meteorological exposure modeling in climate–health research.
Zhang et al. (2026) studied this question.