## 1 Introduction  
As key data for meteorological research and social governance, the value of temperature data should not be overlooked. For an extended period, meteorological organizations have always regarded the collection of temperature data as a crucial task. This is because possessing comprehensive and detailed temperature data plays a decisive role in delving deeper into meteorological phenomena, enabling effective weather forecasting and early warning systems, and optimizing social governance [1, 2, 3, 4]. The accumulation and analysis of temperature data not only provides a robust foundation for meteorological studies but also lays the foundation for the harmonious development of society. With the advancement of technology, various methods for collecting temperature data have emerged. Meteorological organizations have attempted to use Automatic Weather Stations (AWS) [5; 6] or satellite technology (e.g., remote sensing, GPS, etc.) [7]. However, the data collected by these methods have fixed and relatively long intervals. Therefore, collecting temperature data through ground observation stations remains the most direct and reliable method [8; 9]. Meteorological organizations, while relying on this method, also must face some challenges: the distribution of observations is not uniform. This issue can lead to data gaps or biases. As of now, China has established an integrated meteorological observation system comprising over 70,000 ground-based automatic meteorological stations, 236 units of the new generation of weather radars, and seven operational Fengyun meteorological satellites in orbit [10]. Despite the success of this system, China still faces the challenge of unevenly distributed observation stations. As shown in Figure 1, most observation stations are located in economically developed or densely populated regions, such as coastal areas and the central part of the country. In contrast, the western and northern regions have a limited number of stations. This distribution pattern inevitably leads to inconsistent data density, which may introduce new challenges in subsequent climate prediction or research efforts. To address the issue of insufficiently refined data, in addition to using numerical simulation to obtain high spatiotemporal resolution output data [12; 13], numerous studies have attempted to perform super-resolution (SR) operations on low-resolution (LR) data to generate high-resolution (HR) data. Traditional methods primarily rely on interpolation techniques, with optimal interpolation (OI) [14] being the most used. OI combines actual values with physical laws to calculate theoretical values, but as a linear interpolation method, it cannot handle nonlinear data, nor can it incorporate temporal information. Subsequent researchers have proposed bicubic and bilinear interpolation [15], which are polynomial interpolation methods that offer better scalability and adaptability. A significant advantage of interpolation methods is that they do not require a large amount of data for training, but they often result in over-smoothed outcomes and blurred edges. With the advancement of deep learning, research in image SR has also yielded numerous outcomes [16; 17; 18], and the technology has become increasingly sophisticated. An increasing number of researchers are attempting to employ neural networks to address the issues. In 2018, [PERSON] [19] utilized a Super-Resolution Convolutional Neural Network  
Figure 1: Distribution of national ground meteorological stations in China [11]. (SRCNN) for SR operations on rainfall data, achieving a resolution of 0.125\({}^{\circ}\) (12.5 km) through upsampling. In 2020, [PERSON] proposed an Ocean Data Reconstruction (ODRE) model, which is an SR model encompassing multi-scale feature extraction and multi-reception field mapping referred to as the ODRE model [20].