A daily highest air temperature estimation method and spatial–temporal changes analysis of high temperature in China from 1979 to 2018

[摘要] The daily highest air temperature ( T max ) is a keyparameter for global and regional high temperature analysis which is verydifficult to obtain in areas where there are no meteorologicalobservation stations. This study proposes an estimation framework forobtaining high-precision T max . Firstly, we build a near-surface air temperature diurnal variation model to estimate T max with a spatialresolution of 0.1 ∘ for China from 1979 to 2018 based onmulti-source data. Then, in order to further improve the estimation accuracy,we divided China into six regions according to climate conditions andtopography and established calibration models for different regions. Theanalysis shows that the mean absolute error (MAE) of the dataset( https://doi.org/10.5281/zenodo.6322881 , Wang et al., 2021) after correction with thecalibration models is about 1.07  ∘ C and the root mean squareerror (RMSE) is about 1.52  ∘ C, which is higher than that before correction to nearly 1  ∘ C. The spatial–temporal variationsanalysis of T max in China indicated that the annual and seasonal mean T max in most areas of China showed an increasing trend. In summer andautumn, the T max in northeast China increased the fastest among the sixregions, which was 0.4 ∘ C per 10 years and 0.39 ∘ C per 10 years, respectively. The number of summer days and warm days showed an increasing trend in allregions while the number of icing days and cold days showed a decreasingtrend. The abnormal temperature changes mainly occurred in El Niño yearsor La Niña years. We found that the influence of the Indian Ocean basinwarming (IOBW) on air temperature in China was generally greater than thoseof the North Atlantic Oscillation and the NINO3.4 area sea surfacetemperature after making analysis of ocean climate modal indices with airtemperature. In general, this T max dataset and analysis are of greatsignificance to the study of climate change in China, especially for environmental protection.

[学科分类] 土木及结构工程学