Reply on RC1

Abstract

The daily highest air temperature (Tmax) is a key parameter for global and regional high temperature analysis, which is very difficult to be obtained in areas where there are no meteorological observation stations. This study proposes an estimation framework for obtaining high-precision Tmax. Firstly, we build a near surface air temperature diurnal variation model to estimate Tmax for China from 1979 to 2018 based on multi-source data. Then in order to further improve the estimation accuracy, we divided China into six regions according to climate conditions and topography, and established calibration models for different region. The analysis shows that the mean absolute error (MAE) of the dataset (https://doi.org/10.5281/zenodo.5602897) is about 1.07 °C and RMSE is 1.52 °C, which improves the accuracy of the traditional method by nearly 1 °C. The spatial-temporal variations analysis of Tmax in China indicated that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. In summer and autumn, the Tmax in northeast China increased the fastest among the six regions, which were 0.4 °C/10a and 0.39 °C/10a, respectively. The number of summer days and warm days showed an increasing trend in all regions, while the number of icing days and cold days showed a decreasing trend. The abnormal temperature changes mainly occurred in El Niño years or La Niña years. We found that the influence of the Indian Ocean Basin Warming (IOBW) on air temperature in China were generally greater than those of the North Atlantic Oscillation and the NINO3.4 area sea surface temperature after making analysis of ocean climate modal indices with air temperature. In general, this Tmax dataset and analysis are of great significance to the study of climate change in China, especially for environmental protection.