Agricultural production frequently suffers from meteorological risks. Timely and accurately estimating yield losses will greatly improve disaster risk management, especially for a useful tool to transfer risks—agricultural insurance. The weather index insurance products have increasingly become promising alternatives to traditional ones. However, the current single-index insurances, mainly based on a statistical model with a lower explanation ability and at an aggregate scale (e.g. county, province), are lack of mechanistic processes and having huge basis risk. Here, we proposed a new framework to quantify maize production risk by running the well calibrated CERES-Maize driven by various chilling scenarios. We first analyzed the chilling risk across Northeast China, then developed a composite chilling index (CCI) and CCI-based vulnerability curves to derive the corresponding yield losses. Finally, the specific pure premium rates were calculated respectively at 16 sites across the studied areas. The results showed that composite-index insurance ($\overline{RMSE}$ ≤ 721 kg/ha) outperformed (R² improved by 35%) every single index ($\overline{RMSE}$ ≈ 1140 kg/ha) in quantifying yield loss. We believe our proposed framework will benefit insurer companies rapidly estimating yield losses and designing more promising products, and potentially apply into other regions for other crops and hazards to transfer climate risk in the future.

农业生产经常遭受气象风险。及时准确地估计产量损失将大大改善灾难风险管理，尤其是对于转移风险的有用工具（农业保险）而言。天气指数保险产品已逐渐成为传统产品的有前途的替代品。但是，目前的单指标保险主要基于解释能力较低的统计模型，且总体规模较大（例如县，省），缺乏机械化流程，具有较大的基础风险。在这里，我们提出了一个新的框架，通过运行由各种冷藏情景驱动的经过良好校准的CERES-玉米来量化玉米生产风险。我们首先分析了整个东北地区的低温风险，然后开发了一个综合冷却指数（CCI）和基于CCI的脆弱性曲线，以得出相应的产量损失。最后，分别在研究区域的16个站点上计算了特定的纯溢价率。结果表明，综合指数保险（$\overline{\mathrm{[R}\mathrm{中号}\mathrm{小号}E}$≤721 kg / ha ）的每一项指标都优于（R ²提高了35％）$\overline{\mathrm{[R}\mathrm{中号}\mathrm{小号}E}$≈1140 kg / ha）来量化产量损失。我们认为，我们提出的框架将使保险公司迅速估算出产量损失并设计出更有前景的产品，从而使保险公司受益，并有可能在其他地区应用其他作物和危害，从而在未来转移气候风险。