## 4 Discussion  
Similar to comparative studies [11], we simulated and predicted the spatial and temporal variation characteristics of extreme temperature indices in the North China Plain for different periods of history and the future using SVM regression using day-by-day temperature monitoring data from 54 meteorological stations and CMIP6 climate model data processed by statistical downscaling. Based on the CMIP5 global climate model, [PERSON] et al. [2] simulated and predicted the climate of the North China Plain. The results showed that the maximum and minimum temperature increases under both climate scenarios (RCP4.5 and RCP8.5) were similar until 2040. The temperature increase under RCP8.5 was much higher than that under RCP4.5 after 2040. The extreme temperature index in the study area was studied by [PERSON] et al. [28] using the independent weighted mean method based on the CMIP6 climate model. The results showed that the extreme high temperature index tends to increase in the future. In contrast, the extreme low temperature index decreases, and the extreme temperature index under the high radiative forcing scenario (SSP585) has the most significant change. The findings of this study indicate a similar trend, with the four climatic scenarios showing relative changes in the extreme climate index before 2050, and more evident disparities in the changes after 2050, with SSP585 showing the highest evolution and SSP126 leading the slightest difference. However, the comparative forecast findings still contain some discrepancies due to climate model selection and simulation methodology variances. The results of [PERSON] and [PERSON] et al., for example, demonstrate that from 2040, the growth in different climatic scenarios will be more diverse. In contrast, the estimated threshold of this study is 2050. As a result, further research may be carried out by adding or filtering climate models for comparison studies. Meanwhile, while the SVM method is more effective in the simulation and evaluation of extreme temperature indices in the historical period than individual climate models and the arithmetic mean, other machine learning algorithms, such as random forests and decision trees, must be compared to it to find a more suitable simulation algorithm for this study area. Figure 8: Spatial characteristics of changes in extreme temperature indices in the North China Plain from 2061 to 2100 under the SSP585 scenario. Although the simulation method of GCMs in CMIP6 has improved noticeably, there are still some uncertainties in the corresponding future GHG emissions due to variances in the feedback processes of various GCMs, which has a significant impact on the predictability of the findings [20,29]. Furthermore, due to substantial differences in structural principles, climate forcing settings, emission scenario design, and model resolution, the simulation performance of climate change can be significantly different among GCMs. The simulation results may have a bias, which increases the difficulty of future climate simulation and the uncertainty of simulation results. To address these problems, the simulation effect of the CMIP6 climate model can be improved. The first is the downscaling approach, divided into two categories: statistical downscaling and dynamic downscaling. Statistical downscaling establishes a functional relationship between global-scale meteorological factors and regional-scale meteorological factors to obtain more reasonable simulation results, whereas active downscaling nests the regional climate model in the global climate model to receive high-resolution regional climate information [30,31]. The downscaling approach efficiently solves the problem of climate model scale mismatch by converting the output of large-scale, low-resolution GCMs or regional climate models into small-scale, high-resolution regional climatic information. By layering the regional climate model RegCM4.0 into the global climate model in one direction, [PERSON] et al.