## 1 Introduction  
According to the IPCC report [1], the trend of global warming is strong, with a temperature increase rate of 0.13 \({}^{\circ}\)C/10a over the last 50 years. Extreme climate events (heat waves, droughs, etc.) are becoming more common and intense, wreaking havoc on human life, society, the economy, and the environment [2]. In the context of future global warming, the North China Plain, with its flat topography, abundant light and heat resources and being an essential agricultural food production base in China, will most likely be threatened by more severe extreme events in terms of economic population, agricultural production, and ecological environment. As a result, it is critical to fully consider the risk of extreme temperature changes in the North China Plain, improve the prediction of extreme temperature changes of the region, face this serious challenge together, and rationally promote the region's and country's green and sustainable joint development and ecological civilization. The Global Climate Model (GCM) includes fundamental processes in circles of the climate system and can depict material and energy flow between them. It is a valuable tool for delving into the mechanisms of past climate change and forecasting future climate change. The results provide the data needed to study climate change and its effects at multiple spatial and temporal scales and a scientific foundation for human response and adaptation to future climate change [3]. In the 20 years since the CMIP was implemented, the World Climate Research Programme (WCRP) Working Group on Coupled Models (WGCM) has launched the sixth International Coupled Model Comparison Program (CMIP6), which includes the most significant number of participating models, the most abundantly designed numerical experiments, and the most considerable amount of simulated data [4]. The climate system simulations are closer to observed values [5,6]. It has been widely utilized in temperature and precipitation simulations and predictions, among other things [7]. [PERSON] and [PERSON] examined CMIP6 with CMIP5 simulations of temperature and precipitation on the Qinghai-Tibet Plateau, finding that most CMIP6 models performed better than CMIP5 [8]. At the same time, [PERSON] et al. also found that CMIP6 could better reproduce the climatic characteristics of China, and the cold bias was improved compared with CMIP5 [9]. [PERSON] found that the simulated precipitation of the CMIP6 model in China was in good agreement with the observed values. In the future scenario, the average annual precipitation in China showed increased rates of precipitation and fluctuation. In northern China the increased rate of precipitation was higher than in southern China [10]. Extreme climate events will become more frequent and intense due to global warming; therefore, predicting and analyzing the occurrence and impacts of extreme climate events has become a demanding problem in climate science. Traditional mathematical methods are being used in the current scientific investigation on predicting extreme temperatures in the North China Plain. For example, [PERSON] et al. used a statistical downscaling method to obtain daily station-by-station data and estimate future climate change in the North China Plain under two typical climate scenarios (RCP4.5 and RCP8.5) on a multi-model ensemble by downscaling future climate scenarios from 33 GCMs in CMIP5 [11]. Machine learning is a popular study topic in artificial intelligence and pattern recognition. Its theories and methods have been applied to a broad range of issues in engineering and scientific fields. Changes in the meteorological environment have temporal and spatial diversity, and the change process is non-linear variable. Therefore, machine learning approaches can successfully analyze and predict meteorological data [12]. [PERSON] et al.