Daily 2-meter air temperature dataset for the Yangtze and Yellow River source regions at 4-kilometer resolution, 1960–1979

1. This dataset addresses the need for high-precision meteorological drivers in the complex terrain regions of the Yangtze and Yellow River headwaters on the Qinghai-Tibet Plateau. It provides daily 2-meter near-surface air temperature data in NC format at the regional scale for the Yangtze and Yellow River headwaters, with a spatial resolution of 4.0 km (approximately 0.03333°). The temporal range covers January 1, 1960, to December 31, 1979. The development process is as follows: First, a two-year (1960 and 1961) WRF simulation with a spatial resolution of 1/30° was conducted. Second, a downscaling model based on a convolutional neural network (CNN) was trained at the daily scale using the WRF simulation results. This downscaling model comprises four convolutional layers (for feature extraction) and one subpixel convolution layer (for constructing high-resolution data). Model inputs include coarse-resolution air temperature data, coarse-resolution terrain data (i.e., grid-based elevation and elevation standard deviation), and high-resolution terrain data. The output is high-resolution meteorological data. The trained model is then applied to downscale long-term ERA5 reanalysis data, generating high-resolution (1/30°≈4 km) gridded air temperature data (ERA5_CNN). Cumulative distribution function (CDF) bias correction is implemented using source region station observations, and the final regional product is cropped to the boundaries of the Yangtze-Yellow River source area. Data strictly adheres to CF-1.8 / ACDD-1.3 metadata specifications, provided in NetCDF-4 format for seamless integration with Python scientific computing and direct loading in ArcGIS Pro. 2. Data Content and Elements: Variable: T_2m(time, lat, lon) — 2 m daily mean air temperature (°C), standard_name=air_temperature; associated scalar coordinate height=2.0 m; grid_mapping=crs(WGS84). 3. Spatial-Temporal Extent: Longitude 90.547953–103.413333°E, Latitude 32.148290–36.114560°N; Time 1960-01-01 to 1979-12-31 (including leap years). 4. Resolution: Spatial 0.03333° × 0.03333° (approx. 4 km), Temporal Daily. 5. Naming convention: Daily file ERA5_CNN_t2m_4km_daily_YYYYMMDD.nc (time=1). 6. Coordinates: lat/degN, lon/degE in ascending order; CRS: EPSG:4326. 7. Production Background and Method Overview: The Qinghai-Tibet Plateau features significant topographic variations and distinct surface conditions, making it challenging for conventional 0.25° reanalysis or simple interpolation methods to capture local thermodynamic-topographic effects. This dataset employs WRF short-term high-resolution simulations to learn the “high-resolution to low-resolution” mapping relationship, using CNN to capture nonlinear spatial features for statistical downscaling of ERA5 across all time periods. Subsequently, CDF correction is applied using source region station observations (site-scale mapping constructed and IDW spatially interpolated), significantly reducing high-altitude cold biases and enhancing station consistency. 8. Advantages and Features: ① Higher spatial resolution with terrain detail preservation (≈4 km vs 0.25°); ② Combination of physical prior (dynamic downscaling) + deep learning (statistical downscaling) outperforms interpolation-only spatial sharpening; ③ Improved station consistency after bias correction, yielding more reliable extreme events and interannual variability; 9. Applications: Permafrost/active layer thermal status assessment, frost-thaw index and N-factor calculation, watershed hydrological and ecological modeling, regional climate change detection, surface process simulation, and disaster risk assessment.