<b>Optimizing cropland allocation enhances climate resilience more than time management under extreme rainfall</b>

The gridded dataset used in this repository consists of 0.5° × 0.5° random-forest emulator outputs. The emulators were trained on site-year yield data generated with an improved APSIM-Barley configuration that explicitly represents waterlogging effects. For each grid cell and year over 1982–2100, separate models were developed for VT (conventional/normal genotype) and VS (waterlogging-tolerant genotype), using as predictors background climate, soil properties, CO₂, growing degree days, and a suite of extreme temperature and precipitation indices over the growing season. Daily climate forcing was taken from five bias-corrected and statistically downscaled CMIP6 GCMs (CanESM5, CNRM-CM6-1, CNRM-ESM2-1, EC-Earth3, MIROC6) under the SSP5-8.5 scenario, and the dataset provided here is the median across these five GCMs. Together, these emulator outputs provide spatially explicit projections of crop yields and their responses to future extreme climate conditions, with explicit sensitivity to waterlogging processes. The example code used to develop these emulator-based datasets is provided in the GitHub - llinchao/emulator.本仓库所使用的网格化数据集（gridded dataset）为0.5°×0.5°分辨率的随机森林模拟器（random-forest emulator）输出结果。该模拟器基于经改进的APSIM-Barley配置生成的站点-年度产量数据训练所得，该配置可显式表征渍水（waterlogging）效应。针对1982年至2100年的每个网格单元与年度，研究分别为VT（常规/普通基因型，conventional/normal genotype）与VS（耐渍基因型，waterlogging-tolerant genotype）构建了独立预测模型，模型以背景气候、土壤属性、二氧化碳（CO₂）浓度、生长度日（growing degree days）以及生长季内一系列极端气温与降水指数作为预测因子。每日气候强迫（climate forcing）数据取自5个经偏差校正与统计降尺度的耦合模式比较计划第六阶段（CMIP6）全球气候模式（GCM），具体包括CanESM5、CNRM-CM6-1、CNRM-ESM2-1、EC-Earth3、MIROC6，情景设置为SSP5-8.5；本数据集提供的为上述5个GCM结果的中位数。上述模拟器输出可共同提供作物产量的空间显式投影及其对未来极端气候的响应，且对渍水过程具有明确的敏感性。用于构建此类基于模拟器的数据集的示例代码已上传至GitHub仓库llinchao/emulator。