Abstract. Near-surface air temperature (Ta) is an important
physical parameter that reflects climate change. Many methods are used to
obtain the daily maximum (Tmax), minimum (Tmin), and average
(Tavg) temperature, but are affected by multiple factors. To obtain
daily Ta data (Tmax, Tmin, and Tavg) with high
spatio-temporal resolution in China, we fully analyzed the advantages and
disadvantages of various existing data. Different Ta reconstruction
models were constructed for different weather conditions, and the data
accuracy was improved by building correction equations for different
regions. Finally, a dataset of daily temperature (Tmax, Tmin, and
Tavg) in China from 1979 to 2018 was obtained with a spatial resolution
of 0.1∘. For Tmax, validation using in situ data shows that
the root mean square error (RMSE) ranges from 0.86 to 1.78∘, the
mean absolute error (MAE) varies from 0.63 to 1.40∘, and the
Pearson coefficient (R2) ranges from 0.96 to 0.99. For Tmin, the
RMSE ranges from 0.78 to 2.09∘, the MAE varies from 0.58 to 1.61∘, and the R2 ranges from 0.95 to 0.99. For Tavg, the
RMSE ranges from 0.35 to 1.00∘, the MAE varies from 0.27 to 0.68
∘, and the R2 ranges from 0.99 to 1.00. Furthermore, various
evaluation indicators were used to analyze the temporal and spatial
variation trends of Ta, and the Tavg increase was more than 0.03 ∘C yr−1, which is consistent with the general global warming trend.
In summary, this dataset has high spatial resolution and high accuracy,
which compensates for the temperature values (Tmax, Tmin, and
Tavg) previously missing at high spatial resolution and provides key
parameters for the study of climate change, especially high-temperature
drought and low-temperature chilling damage. The dataset is publicly
available at https://doi.org/10.5281/zenodo.5502275 (Fang et al., 2021a).