# A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data
## 4 Experimental Results and Discussion
### Temperature Prediction for Macau Stations Based on CNN Models  
The longitudinal comparative analysis of the model performance under different prediction time frames shows (Table 2) that the model error increases with the prolongation of prediction time. The _RMSE_, _MAE_, and _MRE_ all show an increasing trend, with the _RMSE_ increasing by 0.112 per hour on average, which is in line with the law of accumulation of meteorological prediction errors, indicating that the uncertainty of the model prediction increases with the increase in time span. Despite the rise in error, the model correlation shows a stable performance. The correlation coefficient decays from 0.986 to 0.974 from the current moment to the next 3 h prediction time, but it still outperforms some of the traditional interpolation methods, indicating that the model is able to capture the intrinsic correlation of the data better, even in long-time prediction. Combined with its unique advantage in predicting the future time period and its comprehensive performance in error control and correlation retention, the model has high practical value in the fields of meteorological prediction and geo-environmental analysis and can provide valuable reference for related work. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Lead Time** & **RMSE** &
\begin{tabular}{c} **RMSE Growth** \\ **Rate** \\ \end{tabular} & **MAE** & **MRE (\%)** & **Correlation** \\ \hline
0 h & 0.924 & - & 0.685 & 0.530 & 0.986 \\ +1 h & 1.026 & +11.0\% & 0.756 & 0.588 & 0.983 \\ +2 h & 1.131 & +22.4\% & 0.831 & 0.653 & 0.979 \\ +3 h & 1.260 & +36.4\% & 0.944 & 0.725 & 0.974 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of deep learning models for temperature prediction in Macau with 0–3 h lead times using ERA5 reanalysis data. Figure 4: (**a**) Temperature timing diagram under instantaneous prediction. (**b**) Same as subfigure (**a**) but focused on the period from 1 February to 31 March 2022. (**c**) Comparison of instantaneous prediction with three interpolation methods. (**d**) This is the same as subfigure (**c**) but focused on the period from 1 February to 1 March 2022. In the time-series analysis section, the decay pattern of prediction accuracy with the prediction step size is systematically revealed by constructing the 0-3 h time scale prediction comparison graph (Figures 5 and 6). The model fitting effect is significantly better than that of the traditional interpolation method, and the prediction curves are highly consistent with the measured temperature trends: whether it is the long-term trend prediction over a two-year time span or the short-term fluctuation simulation over a three-month time window, the key features of the temperature changes can be effectively captured. Figure 5: Comparison of time-series prediction maps under different prediction timings (**a**) for the current moment, (**b**) for the next 1 h, (**c**) for the next 2 h, and (**d**) for the next 3 h.  
Figure 7 shows that the training loss is larger than the validation and test losses. This is because the training loss includes the errors from model and dropout, whereas in validation and test modes, the loss only reflects the model errors without dropout. On the other hand, the validation and test losses are gradually separated as the prediction time step continues to increase. This phenomenon implies that the difference between the model's performance on the validation and test sets gradually increases as the prediction time span increases. The reason for this may be that the uncertainty and complexity in the data increase significantly as the prediction time span expands, and although the model learns on the training set, its generalization ability is challenged in the face of the diverse data characteristics of the validation set and the test set in long time scales.