# A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data
## 1 Introduction  
As an important cornerstone of social development, the influence of modern weather forecasting has permeated many fields, including agricultural production, transportation, disaster prevention and mitigation, and public safety [1]. Nearly 150 years ago, the public weather forecasting service started the prelude of modern meteorology, and since then the discipline has taken forecasting technology as its core development direction [2]. Accompanied by the accumulation of long-term practical experience and the rapid progress of science and technology, human beings are facing the emerging new issues and increasing technical challenges in the field of meteorological forecasting in the process of continuously improving the theoretical system of atmospheric science [3]. Existing gridded temperature datasets are limited by the problem of coarse spatial resolution, which makes it difficult to meet the application needs of more fine-grained geographical temperature data. Although high-resolution atmospheric models can theoretically provide more accurate simulation data, their high computational cost makes it difficult to run them continuously for a long period of time in order to construct reliablehistorical datasets [4]. In contrast, reanalysis data, with the advantage of assimilation technology, can provide long-term continuous and large-scale spatial and temporal coordinated temperature field data, although the spatial resolution is relatively low, and this property makes it more useful in climate-scale studies. Reanalysis data have a wide range of applications in atmospheric science, with ERA5 being one of the most commonly used types of reanalysis data, which covers the globe with a spatial resolution of 0.25\({}^{\circ}\), spanning the period from 1940 to the present, and contains variables commonly used in the atmosphere, including temperature and pressure. In this study, a deep learning temperature prediction model incorporating geospatial features is constructed to address the problem of insufficient spatial resolution (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) of ERA5 reanalysis data in regional-scale applications, especially considering the current situation that special geographic units such as Macau are not in the grid nodes, resulting in the limited accuracy of traditional interpolation. There are significant bottlenecks in the current ERA5 reanalysis data. The spatial resolution of the current open datasets can hardly support the demand of urban microclimate research, especially in complex terrain areas such as Guangdong, Hong Kong, and Macau Greater Bay Area. To obtain high-spatial-resolution data, existing studies mostly use traditional spatial interpolation methods such as bilinear interpolation and cubic spline interpolation to obtain off-grid point data, but they ignore the effect of nonlinearity. For this reason, this study innovatively compares convolutional neural network (CNN) with classical interpolation methods systematically to break through the application limitations of traditional methods. After breaking through the traditional image recognition field, the unique value of convolutional neural network (CNN) in time series prediction has gradually emerged [5]. Through the sliding scanning of the convolutional kernel in the time series dimension, CNN can adaptively extract local correlation features in the data: a small-scale convolutional kernel (e.g., 3 steps) can capture intra-day periodic fluctuations in the temperature series, whereas a large-scale convolutional kernel (e.g., 30 steps) can identify quarterly trend changes [6], which significantly improves the modeling accuracy of scenarios such as weather forecasting. Its parameter-sharing feature dramatically reduces model complexity, and the parallel convolutional operation accelerated by GPU improves the training efficiency of millions of weather station point data by 2-3 orders of magnitude, effectively alleviating the high-dimensional catastrophe problem of the traditional fully connected network [7].