# Predicting Runoff from the Weigan River under Climate Change
## 3 Results
### Evaluation of Temperature and Precipitation Simulation Capability and Future Prediction
#### 3.1.2 Precipitation  
When comparing the precipitation data simulated by each model, the \(r\)-MME data and the assessment of the observed data (Figure 2c), it is clear that the simulation effect of the precipitation data simulated by the MRI-ESM2-0 model is relatively the best, with a correlation coefficient as high as 0.937, showing strong correlation with the observation series. The simulation effect of the CNRM-ESM2-1 model and the CanESM5 model is relatively the poorest, with a correlation coefficient of only 0.428. The correlation coefficients of the precipitation data of the remaining four models (ACCESS-ESM1-5, INM-CM5-0, BCC-CSM2-MR and CMCC-ESM2) are in the range from 0.483 to 0.572, thus showing moderate correlations. The \(r\)-MME precipitation data have a correlation coefficient of up to 0.837,  
Figure 3: Historical temperature data (**a**) and \(r\)-MME temperature prediction (**b**) situation. The solid, coloured lines in (**b**) represent the \(r\)-MME data for each scenario, and the shaded areas represent the range of plus or minus one standard deviation of the annual average temperature for each scenario.  
which shows strong correlation. It can be seen that most of the GCMs generally simulate temperature data better than precipitation data, except for individual models that optimally simulate precipitation. The \(r\)-MME method can reduce the variability among models, which enables the simulation results to have higher stability and accuracy compared to the use of single-model data.  
A comparison of precipitation variations in the historical observed data, the data of each model and the \(r\)-MME data in the study area is shown in Figure 4a. The observed precipitation series showed a fluctuating upward trend, increasing from 301.55 mm in 1961 to 338.94 mm in 2014, with an average growth rate of 0.71 mm/a. The average annual precipitation during the historical period was 382.62 mm. The annual precipitation simulated by the GCMs ranged from 361.54 mm to 469.64 mm. The CNRM-ESM2-1 model's precipitation value is overall 87.02 mm larger than the observed value, and the rest of the model data basically fluctuated up and down around it. However, the CNRM-ESM2-1 model data were not excluded from the \(r\)-MME of the GCM data because the trend of the CNRM-ESM2-1 model is more similar to that of the observed series and most of the values of the precipitation data predicted by the other models are smaller than those of the observed data. The prediction of future precipitation changes based on the \(r\)-MME data (Figure 4b) shows an increasing trend in \(r\)-MME precipitation under all radiative forcing scenarios from 2020 to 2050. Under the SSP1-2.6 scenario, the \(r\)-MME precipitation increases to 361.56 mm in 2050, which is the slowest increase of 2.63 mm. The increase under the SSP2-4.5 scenario and SSP3-7.0 scenario is 14.89 mm and 45.09 mm, respectively, during the next 30 years. Under the SSP5-8.5 radiative forcing scenario, it increases from 363.60 mm in 2020 to 428.63 mm in 2050, with an average annual increase of 2.17 mm, which is the fastest growth rate.  
Figure 4: Historical precipitation data (**a**) and \(r\)-MME precipitation prediction (**b**) situation. The solid, coloured lines in (**b**) represent the \(r\)-MME data for each scenario, and the shaded areas represent the range of plus or minus one standard deviation of the annual average precipitation for each scenario.