## 2 Materials and Methods
### Data Sources  
The daily temperature observations at 54 meteorological stations (Figure 1) in the study area were derived from the China Meteorological Science Data Service Sharing Network's ([[https://data.cma.cn/](https://data.cma.cn/)]([https://data.cma.cn/](https://data.cma.cn/)), accessed on 1 January 2022) monthly surface precipitation and temperature value grid point dataset (V2.0), which includes four major climate factors: maximum temperature, minimum temperature, total solar radiation, and rainfall. The dataset is based on a 0.5 \(\times\) 0.5\({}^{\circ}\) horizontal resolution spatial interpolation of data from more than 2400 ground stations throughout China [18].  
The model data are the historical climate simulation and future scenario experimental data from the CMIP6 climate model. The future scenario experimental data are a combination of different representative concentration pathways (RCPs) and shared socioeconomic pathways (SSPs) [19]. RCPs are a series of enrichment and comprehensive emissions scenario [20] of a 21 st century under the influence of human activities on climate change prediction model input parameters, to describe the future population, social economy, science and technology, energy consumption and land use change, greenhouse gases, reactant  
Figure 1: Spatial distribution of meteorological stations in the study area.  
gas and aerosol emissions, as well as the concentration of the atmospheric composition. CMIP6 not only upgrades RCP2.6, RCP4.5, RCP6.0, and RCP8.5 to SSP126, SSP245, SSP460, and SSP585, but it also provides more options for scientists in future climate prediction. The new emission modes also include SSP119, SSP434, SSP534 OS, and SSP370. CMIP6 is used in many models, and seven models were chosen for this investigation from the historical and future scenario data releases ([[https://esgf-node.llnl.gov/search/cmip6](https://esgf-node.llnl.gov/search/cmip6)]([https://esgf-node.llnl.gov/search/cmip6](https://esgf-node.llnl.gov/search/cmip6)), accessed on 1 January 2022), with model characteristics provided in Table 1. The following four typical climate scenarios were chosen for this study, representing four climatic pathways of (1) sustainable development, (2) moderate development, (3) local development, and (4) conventional development, based on the representativeness of future climate scenarios and the completeness of data under the corresponding climate scenarios:  
* SSP126 is a scenario with a stable radiative forcing of 2.6 W/m\({}^{2}\) in 2100 [21];
* SSP245 is a medium forcing scenario, and the radiative forcing is stabilized at 4.5 W/m\({}^{2}\) in 2100;
* SSP370 is a net radiative forcing scenario with a stable radiative forcing of 7.0 W/m\({}^{2}\) in 2100, which represents a mixture of high social vulnerability and relatively high perceived radiative forcing [22];
* SSP585 achieves a strong forcing scenario in 2100, with an anthropogenic radiative forcing of 8.5 W/m\({}^{2}\).  
The selected month-by-month raster data of seven GCMs are downscaled temporally and spatially in this work to obtain daily site data. The Wagga Wagga Institute of Agriculture in Australia developed a statistical downscaling method [11; 23] that can effectively downscale the output of GCMs to a fine spatial and temporal scale to enhance the prediction of future extreme temperature events. Unlike many other statistical downscaling methods requiring data regarding atmospheric circulation or sea surface temperature as predictors, it contains spatial and temporal downscaling components. The approach can generate daily data series in real-time from monthly gridded GCMs. Compared to dynamic downscaling, it can adjust for biases in GCM data and is computationally efficient enough to be applied to many GCMs. As a result, the approach can be used to investigate future climate change uncertainty.