Modeling of extreme weather conditions is an important issue in environmental risk assessment, management and protection. In this paper, Annual and monthly maxima of daily temperature and rainfall data dating from 1901–2016 and 1950–2018 are characterized for nine countries within the greater horn of Africa. The generalized extreme value (GEV) distribution is fitted to the data sets from each of the nine countries using the method of maximum likelihood. Both the location and scale parameters of the GEV is formulated as a function of time to account for variability and trends in the extremes of temperature and rainfall so that their future behavior can be predicted. Based on our results, We provide return levels for the years 2, 10, 50 100, and 200 which could be used as measures of flood protection. To understand the spatial cross-correlation patterns on different time scales in each country and how rainfall and temperature are related to agricultural variables, we utilize the detrended cross-correlation coefficient$\left({\rho }_{\mathit{DCCA}}\right)$ and its generalization, $\left({\mathit{DMC}}_x^2\right)$ to account for that, respectively. Given the results of the $\left({\mathit{DMC}}_x^2\right)$ and the fact that climate extremes pose a huge threat to agriculture and food security, we employed copula based models to describe the structure of dependence between climatic variables and the crop related variables such as yield and production quantity. The results from the copula analysis show that irrespective of the country, climatic factors and the agricultural products(production/yield) the strongest dependence is demonstrated by the pairs involving cereal crops, while the weakest dependence is characterized by the pairs involving regular potato.

极端天气条件的建模是环境风险评估，管理和保护中的重要问题。在本文中，以非洲大角内的9个国家为特征，描述了1901-2016年和1950-2018年的每日温度和降雨量的年度和每月最大值。使用最大似然法将广义极值（GEV）分布拟合到来自9个国家/地区的数据集。GEV的位置参数和尺度参数均被设计为时间的函数，以考虑温度和降雨极端情况的变化和趋势，以便可以预测其未来行为。根据我们的结果，我们提供了2年，10年，50 100年和200年的回报水平，可以用作防洪措施。$（{\rho }_{\mathit{DCCA}}）$ 及其概括， $（{\mathit{DMC}}_X^{\mathrm{2个}}）$分别说明这一点。鉴于结果$（{\mathit{DMC}}_X^{\mathrm{2个}}）$以及极端气候对农业和粮食安全构成巨大威胁这一事实，我们采用基于copula的模型来描述气候变量与作物相关变量（如产量和产量）之间的依存关系。copula分析的结果表明，与国家，气候因素和农产品（生产/产量）无关，对谷物作物的依赖程度最高，而对常规马铃薯的依赖程度最弱。