## 4 Experiments and Results
### Two-Times Downsampling (0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) LR Data)  
We performed an experiment on LR data (0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)) generated through downsampling of original data by a factor of 2. As illustrated in Figure 6, our deep learning model processes 50 \(\times\) 50 inputs to generate outputs at 100 \(\times\) 100 resolution, effectively achieving a 2\(\times\) super-resolution reconstruction. As shown in Figure 8, the model converged after approximately 70 epochs of training at a resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\), with the loss dropping to a low and stable state. Figures 9 and 10 display the SR reconstruction of 2 m temperature data for a single moment (31 December 2020--23:00) and the average for the entire year of 2020 in the validation dataset, respectively. In the first instance, we can observe the specifics of LR data, SR data, and true data. The second column presents the differences among the three types of temperature data. Through these differences, we can directly observe and analyze the discrepancies between each observation point (pixel) in the region, including whether there are severe deviations in different areas and the direction of these deviations.  
Upon comparison, we deem the model's SR reconstruction performance to be excellent at a resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\). The differences between SR data and true data at a single moment (Figure 9e) are relatively small, with deviations occurring only in inland areas. However, these deviations are distributed uniformly and have small magnitudes, without any large-scale regions showing a consistent temperature bias that is either too high or too low. We consider this to be within the normal range of errors. The model has effectively captured the temperature variation characteristics within the region, demonstrating good performance in the SR reconstruction of temperature data for this area. Comparing the differences between true data and LR data (Figure 9d) with the differences between SR data and LR data (Figure 9f), it can be observed that the overall patterns are quite similar, with the main deviation areas and directions being consistent between the two images (the areas designated by the red dotted lines in Figure 9d,f).  
Figure 8: Model training results at 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) resolution.  
Figure 9: SR results for a single moment at a resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\): **(a)** LR temperature data; **(b)** SR temperature data; **(c)** true surface temperature data; **(d)** comparison between true data and LR data; **(e)** comparison between true data and SR data; **(f)** comparison between SR data and LR data. The areas where the LR data differs greatly from the SR data and the real data are indicated by the red dotted line.  
Figure 10 illustrates the average SR performance of temperature data over the year 2020, where the model demonstrates an excellent overall performance. It is noticeable that the gap between the SR data and the actual data is significantly smaller over extended periods. We hypothesize that this is because minor errors at individual moments are averaged out, leading to an improved overall performance when considering the data over a longer time frame.