# A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data
## 2 Data  
The European Centre for Medium-Range Weather Forecasts (ECMWF), as an authoritative organization in the field of global meteorological reanalysis, has gone through many generations of technological evolution in its reanalysis data product system: from laying the foundation of the first Global Atmospheric Research Program (FGGE) in 1979, to the release of the first 15-year reanalysis dataset ERA-15 (1978-1994) in the 1990s, and then the launching of the cross-century dataset ERA-40 (1957-2002) and its improved version ERA-Interim (1979-2019) [10], culminating in the current most representative fifth-generation reanalysis product, ERA5, which covers the period from January 1940 to the present, with a spatial resolution of 0.25\({}^{\circ}\) (about 31 km) and a temporal resolution of 1 h, including many meteorological variables, such as temperature, humidity, wind speed, etc. [11]. In this study, the air temperature at 2 m above the surface (variable identified as VAR_2T, hereinafter referred to as temperature) from the ERA5 for the period 2012-2022 is used. As shown in Figure 1, the data were focused on a specific range around Macau, i.e., the area covered by latitude 17-27\({}^{\circ}\) N and longitude 109-119\({}^{\circ}\) E, with a total of 41 \(\times\) 41 grid points, as indicated by the intersection of the grid lines. For Macau, a typical tropical monsoon climate zone, the ERA5 data can accurately characterize the spatial heterogeneity of its climatic features--and its high temporal resolution makes it possible to capture climatic fluctuations in short time scales, while its high spatial resolution helps to reveal the climatic differences between different regions [12], which provide a reliable data base for assessing the regional climatic evolution and its impacts on the urban operation. As an important node in the Guangdong-Hong Kong-Macau Greater Bay Area, the refined distribution data of meteorological elements in the Macau region have an important decision support value for extreme weather warning, urban planning, and ecosystem management. The real temperature values are based on the 2012-2022 measured temperature series from the Tai Tam Shan Meteorological Station (22.16\({}^{\circ}\) N, 113.57\({}^{\circ}\) E, altitude 112 m) in Macau. The data from this station fully characterize the typical features of the subtropical monsoon climate of Macau: in summer (June-August), controlled by the subtropical high pressure, the monthly mean temperature is 28.6-31.9 \({}^{\circ}\)C; in winter (December-February), influenced by the East Asian winter winds, the monthly mean temperature is 16.2-19.5 \({}^{\circ}\)C, which constitutes the seasonal baseline validation scenario of the temperature prediction model. In this study, two datasets are used for analysis. The ERA5 reanalysis dataset provides gridded air temperature at 2 m above the surface and is used as the model input. Observational data from the Macau meteorological station serves as the label for supervised learning. The temperature variable in the ERA5 reanalysis data is stored in Kelvin (K), an international standard unit system. In order to adapt the observation data (unit: \({}^{\circ}\)C) from the Macau meteorological station, it is necessary to perform the temperature scale conversion: based on the standard formula T (\({}^{\circ}\)C) = T (K) \(-\) 273.15 defined by the International Committee for Weights and Measures (CIPM) [13], the vectorization operation is implemented on the dataset, and the batch conversion from Kelvin to Celsius is realized. To solve the problem of time matching between the ERA5 reanalysis data of the European Meteorological Centre (ECMWF) and the meteorological observations of Macau, a standardized alignment method is used. Time zone and format differences are eliminated by standardizing the date format (complementary eight-digit specification) and the hourly rounding mechanism (24 o'clock is automatically converted to the next day's 0 o'clock).