## 1 Introduction  
Since the 18 th century, greenhouse gas emissions have been increasing as a result of industrialization and other human activity, leading to global warming and climate change. According to the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5), global average temperatures would rise by approximately 3.7 \({}^{\circ}\)C around the end of the 21 st century because of global warming. Also, the frequency of high temperatures will increase further [1]. Global climate change causes a variety of extreme weather conditions, such as drought, heat and cold waves, and heavy rain, which have broad impacts on ecosystems and human society. Extreme weather phenomena have become increasingly frequent; however, it is difficult to predict due to the variability of the patterns of climate change on a global scale. Moreover, climate change is very closely related to water supply and food security. In 2010, more than 20% of Russian agricultural areas were affected by drought and heatwave; in response, the Russian government issued a wheat embargo in 2010-2011, leading to increases of up to 50% in the international wheat price [2; 3]. Thus, extreme weather can trigger spikes in crop prices, leading to instability in the global food supply [4]. In the season when grain filling occurs, drought and heatwaves can have acute effects on crop growth and production [5; 6]. Therefore, developing a reliable crop yield model is essential for deciding agricultural policy and ensuring food security. [PERSON] et al. [7] mentioned that global corn and soybean yields could drop by approximately 5.5% and 3.8%, respectively, because of climate change. [PERSON] et al. [5] presented that substantial crop yield losses due to extreme temperatures can seriously affect the global food supply. [PERSON] et al. [4] showed that drought and heatwave reduced national cereal production by 9-10%; the effects of drought lasted longer than those of heatwave, reducing both crop yield and harvested area. [PERSON] et al. [8] analyzed climate change impacts on the yields of spring wheat, canola, and maize in Canada using multiple crop models under four different scenarios for global warming levels: 1.5 \({}^{\circ}\)C, 2.0 \({}^{\circ}\)C, 2.5 \({}^{\circ}\)C, and 3.0 \({}^{\circ}\)C. The results show that the yield changes would be various according to crop type. Small increases for canola and spring wheat and some decrease for maize would be expected under the warming scenarios. These previous studies demonstrated the impacts of extreme weather on actual crop yields, but they did not sufficiently present the prediction capability of the model under extreme weather conditions. Although challenging, crop yield prediction can provide useful information for adapting to changing Earth environments. A robust model for predicting crop yields under extreme weather conditions is needed for future agricultural management. As efficient ways to predict crop yield, artificial intelligence (AI) models such as random forest (RF), support vector machine (SVM), and deep neural network (DNN) have been examined in many studies. [PERSON] et al. [9] used climate and soil data for the prediction of wheat and corn yields and showed that the accuracy of the AI model was better than that of classic linear equations. [PERSON] and [PERSON] [10; 11] used vegetation indices and climate data for corn yield prediction in the United States (US); they mentioned that the result from a DNN model was best among multiple AI models. [PERSON] et al. [12] examined six AI models for predicting crop yield in the Midwestern US, which showed that an optimized DNN model outperformed the other five models. However, the performance of AI models for crop yield prediction may depend on the data used in the experiments. Particularly under extreme weather conditions, the dataset can have somewhat different characteristics from the usual pattern.