## 1 Introduction
In 2022, [PERSON] introduced Spectral Normalization (SN) to improve the Generative Adversarial Networks (GANs) architecture, enhancing the quality of real-time analysis and data transmission delays and improving the model's performance in SR of sea surface temperature (SST) data [21]. [PERSON] proposed a method to enhance the resolution of near-surface climate models using the Fourier Neural Operator (FNO) [22]. The study introduced the Clausius-Clapeyron equation, incorporating physical constraint loss to better constrain the relationship between state variables. Ultimately, experiments demonstrated that FNO also exhibited comparable performance to deep learning models. In addition to the correlation between regional temperatures, [PERSON] proposed the T_INRI model, integrating temporal scale information into feature information [23]. The research is based on Time-aware Implicit Neural Representation, which calculates the four surrounding true values by position weighting for unknown values, eliminating data discontinuity issues in the process of generating high-resolution data. In 2024, [PERSON] proposed a deep learning model called dilated Adaptive Deep Residual Network for Super-Resolution (dADR-SR) for SR operations on SST datasets [24]. The dADR-SR leverages convolutional layers with different dilation factors to capture features at various scales while incorporating Adaptive Residual Blocks (ARBs) within the model, allowing for self-adjustment of feature representation and enhancing the model's response to input data. Subsequently, researchers aimed to refine the details and quality of low-resolution images using the diffractive model, employing the SR3 model and ResDiff architecture to improve the spatial resolution of meteorological data [25]. The model integrates convolutional filters that simulate finite difference schemes to accurately capture the learning of physical processes. However, the model is sensitive to data noise, especially when the quality of LR data is poor. The contribution of this paper is the construction of a ResNet-Subpixel neural network, which takes LR data of varying sizes as its input and outputs HR data reconstructed through super-resolution, thereby enhancing the effective resolution and detailed information of the data. In traditional numerical models, the time step and the interval lengths of spatial coordinates are limited by the Courant-Friedrichs-Lewy (CFL) condition [26; 27; 28]. According to the CFL expression:  
\[u\frac{\Delta t}{\Delta x}\leq\mathrm{C} \tag{1}\]  
where C is the Courant number (a dimensionless constant), and \(u\) is the constant velocity magnitude. Therefore, when the longitudinal and latitudinal resolutions are doubled, the temporal resolution must also be doubled. When the numerical model is used to obtain an output with twice the original spatial resolution, the computational cost increases by a factor of \(2^{3}\) (as the resolution doubles in longitude, latitude, and time). By using the proposed super-resolution framework, resolution enhancement is achieved through the proposed framework based on the low-resolution output from the numerical model. In this case, the computational cost of the numerical model is reduced to 1/8. The network ensures the effective resolution and detail of the data while requiring less time and computational resources compared to traditional numerical models. The model performs well on LR data with different downsampling, which enhances its versatility. Comparisons with traditional interpolation methods for expanding information are made to argue the advantages of machine learning methods for temperature data in meteorological applications.