Decade 1km resolution monthly temperature data for China

Air temperature near the surface is an important controlling parameter for land surface processes, and is crucial to ecological, environmental, and hydrological modeling. Temperature records observed at meteorological stations have been widely used, but there has been an increasing need for temperature data in grid for modeling especially those using remote sensing data. Although grid temperature can be estimated from in-situ temperature records using interpolation algorithm, low accuracy has been found in the estimation due to limited ground stations, especially when there were insufficient sites to represent all land cover types and terrain conditions in the area. NCEP/NCAR reanalysis project produced climate variables uses a frozen state-of-art global data assimilation system and a database as complete as possible. Although resolution of the NCEP data has a coarse resolution (0.5 degree), it provides global, consistent, and long term estimation of climate variables. This paper presents a downscaling approach for deriving monthly temperature at 1km resolution from the NCEP by utilizing derived relationships between monthly aggregated NCEP temperature and other ground elements, such as terrain, vegetation, and geographic locations. Regression tree model was chosen for detecting the possible relationships. Monthly temperature with 1km resolution for China land area has been produced using the approach. The final predicted temperatures were compared with observed records at 380 meteorological stations in China. The results indicated that the downscaled estimations can represent the spatial distribution and trends and the magnitude of temperature in inter-month, with R²ranging from 0.861 to 0.948, and RMSE from 1.88°C to 2.681°C.