# Predicting Runoff from the Weigan River under Climate Change
## 3 Results  
### Evaluation of Temperature and Precipitation Simulation Capability and Future Prediction  
#### 3.1.1 Temperature  
The simulation ability of the selected climate models, the \(r\)-MME maximum and minimum temperature data was evaluated by using Taylor diagrams (Figure 2a,b) to more comprehensively assess the simulation results after downscaling the data of each model. Compared with the maximum temperature observation series, the simulation effect of the ACCESS-ESM1-5 model in terms of maximum temperature is relatively better, and the correlation coefficient can reach up to 0.776, showing a strong correlation with the observed data. The simulation effect of the CNRM-ESM2-1 model is relatively poorer than that of the CanESM5 model, and the correlation coefficient only reaches about 0.440. The simulation effects of the remaining four models (BCC-CSM2-MR, INM-CM5-0, MRI-ESM2-0 and CMCC-ESM2) are not much different, with correlation coefficients ranging from 0.524 to 0.559, thus showing moderate correlations with the observed data. In the lowest-temperature simulation scenario, the ACCESS-ESM1-5 model has the best simulation effect, with a strong correlation coefficient as high as 0.899. The CNRM-ESM2-1 model has, relatively, the worst simulation effect, with a weak correlation coefficient, and the correlation coefficients of the INM-CM5-0 model and the CanESM5 model are around 0.730, demonstrating a good simulation effect. The correlation coefficients of the remaining three models (MRI-ESM2-0, BCC-CSM2-MR and CMCC-ESM2) are between 0.613 and 0.674, showing relatively good correlations. The _r_-MME correlation coefficients of the maximum and minimum temperature data are 0.846 and 0.902, respectively, and the correlations are all relatively significant. In summary, the simulation effect of the models for the minimum temperature is better than that for the maximum temperature, and there are some differences in the simulation effects of different GCMs regarding the maximum and minimum temperatures. The simulation results of _r_-MME data for temperature are better than single-model data. A comparison of temperature changes in the study area from 1961 to 2014 based on the observed data, each model's data and _r_-MME data is shown in Figure 3a. The maximum and minimum temperature data simulated by each GCM showed a trend of fluctuating and increasing, with the annual average maximum temperatures ranging from 8.39 \({}^{\circ}\)C to 8.43 \({}^{\circ}\)C and the annual average minimum temperatures ranging from \(-\)4.49 \({}^{\circ}\)C to \(-\)4.44 \({}^{\circ}\)C, and the upward trend of the observed series of minimum temperatures was more significant than that of the maximum temperature series. The annual average maximum and minimum temperatures of the observed series were 8.20 \({}^{\circ}\)C and \(-\)4.31 \({}^{\circ}\)C, respectively. In the observed temperature series, the maximum temperature increased to 9.45 \({}^{\circ}\)C in 2014, with an average rate of increase of 0.03 \({}^{\circ}\)C/a. The minimum temperature increased from \(-\)4.80 \({}^{\circ}\)C to \(-\)3.56 \({}^{\circ}\)C, with an average rate of increase of 0.02 \({}^{\circ}\)C/a. The annual mean  
Figure 2: Results of the assessment of the modelled values of maximum temperature (**a**), minimum temperature (**b**) and precipitation (**c**) compared to the observed values. maximum and minimum temperatures of the \(r\)-MME data were 8.40 \({}^{\circ}\)C and \(-\)4.47 \({}^{\circ}\)C, respectively, and compared to the observed series, the maximum temperature of the \(r\)-MME series was higher by 0.2 \({}^{\circ}\)C and the minimum temperature was lower by 0.16 \({}^{\circ}\)C. The projected temperature change using the \(r\)-MME data for 2020-2050 (Figure 3b) demonstrates that the maximum and minimum temperatures based on the \(r\)-MME data under the four radiative forcing scenarios show an increasing trend.