Reconstruction of 0.05° all-sky daily maximum air temperature across Eurasia for 2003–2018 with multi-source satellite data and machine learning models

The Eurasian continent is highly vulnerable to climate change. However, there is a lack of high spatiotemporal resolution temperature datasets to support climate change analysis in this region. In this study, an all-sky daily maximum air temperature (Tₘₐₓ) product at 0.05° spatial resolution across Eurasia for 2003–2018 was developed. This product is generated using a satellite-derived model, including parameters such as daytime and nighttime land surface temperature (LST), downward shortwave radiation, net radiation, leaf area index, enhanced vegetation index, and albedo. The study area, Eurasia, was divided into seven regions using a data-driven method. Four machine learning methods, histogram-based gradient boosting (HGB), extremely randomized trees (ET), random forest (RF), and deep belief network (DBN) were employed to train Tₘₐₓ estimation models using 4476 stations from GHCN, GSOD, and CMDC. HGB was finally selected since it exhibited the highest estimation accuracy, the determination coefficient (R²) and root-mean-square-error (RMSE) of the HGB model are 0.984 and 1.736 °C with non-missing values in datasets and 0.985 and 1.812 °C with missing values respectively. The impact of the different situations of LST features on HGB models was tested. LST features containing interpolated LST and GLDAS Tₐ are considered the best. The spatial and temporal accuracy of HGB models was then examined across different land cover types, latitudes, elevations, and months. The permutation test was employed to examine the contributions of different features. Finally, HGB models trained using the best LST features were used to generate the Tₘₐₓ products. In comparison with existing temperature products, the R² and RMSE values were reported as 0.980 and 2.177 °C, indicating strong competition among existing Tₘₐₓ products. In summary, our study provides a scheme for estimating parameters with missing feature values in a consistent manner and provides a solid foundation for the environmental and climate changes studies.