Global gridded crop models (GGCMs) are increasingly used for climate risk assessments and adaptation planning in agriculture. GGCM historical simulation performance is therefore crucial for such applications. However, GGCM performance is lower for rice than for other crops at an aggregated administrative unit level despite the lack of a clear difference in model performance at the site level. Here, we present key factors that need to be considered in the spatial and temporal aggregation of GGCM outputs to improve the evaluation of GGCM historical rice simulations at the country scale. The factors include an adjustment for the harvesting year in GGCM rainfed and irrigated simulations, the removal of misreports from reference data, a consideration of the quality of national crop statistics (census reports), and the explicit incorporation of a planting window. The effect of each individual factor is demonstrated by analyzing a multi-GGCM dataset and performing a planting date ensemble simulation of two GGCMs. We reveal that among others, aligning the harvesting year in the GGCM simulations with that in national reports is pivotal. Although our analysis focuses specifically on rice, the findings of this study are useful for improving the country-level evaluations of GGCM historical simulations for other crops.

全球网格作物模型 (GGCM) 越来越多地用于气候风险评估和农业适应规划。因此，GGCM 历史模拟性能对于此类应用至关重要。然而，尽管在站点级别模型性能缺乏明显差异，但在汇总行政单位级别，水稻的 GGCM 性能低于其他作物。在这里，我们提出了在 GGCM 输出的空间和时间聚合中需要考虑的关键因素，以改进在国家范围内对 GGCM 历史水稻模拟的评估。这些因素包括在 GGCM 雨养和灌溉模拟中对收获年份的调整、从参考数据中去除错误报告、考虑国家作物统计数据（普查报告）的质量、以及明确纳入种植窗口。通过分析多 GGCM 数据集并执行两个 GGCM 的种植日期集成模拟，证明了每个单独因素的影响。我们揭示，除其他外，将 GGCM 模拟中的收获年份与国家报告中的收获年份保持一致至关重要。尽管我们的分析专门针对水稻，但本研究的结果有助于改进 GGCM 历史模拟对其他作物的国家级评估。