A global dataset of daily near-surface air temperature at 1-km resolution (2003-2020)

Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax and Tmin) is particularly valuable and critically needed in the scientific and policy communities, but is still not available. In this paper, we developed a global dataset of daily Tmax and Tmin dataset at 1-km resolution from 2003 to 15 2020 through the combined use of station-based ground Ta measurements and satellite observations (i.e., digital elevation model, and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors of our estimates ranged from 1.20 to 2.44 for Tmax and 1.69 to 2.39 ºC for Tmin. We found that the accuracies were affected primarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents the negative and positive relationships between Ta with elevation or land surface temperature; it 20 captured spatial and temporal patterns of Ta realistically. This global 1-km gridded daily Tmax and Tmin dataset is the first of its kind and we expect it to be of great value to global studies such as urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data are available at Iowa State University’s DataShare at https://doi.org/10.25380/iastate.c. 6005185 (e.g., an unrealistically positive relationship between Ta and elevation), thereby leading to large uncertainties or even wrong results. To overcome such drawbacks, we recently proposed a class of Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP) (Kim et al., 2021; Zhang et al., 2022b), and these can capture and preserve relationships between Ta and explanatory variables. The SVCM-SP algorithm was originally 70 implemented for estimating Ta over mainland China, with significant improvement in terms of both accuracies and computational efficiency compared to alternative methods such as GWR (Zhang et al., 2022b). The potential of SVCM-SP as a routine algorithm to generate global Ta products is still untapped and addressed here. Here we aim to generate a global dataset of daily maximum and minimum Ta (Tmax and Tmin) at 1 km across 50°S ~79°N from 2003 to 2020 by integrating ground-based Ta measurements from weather stations and gridded satellite-observed covariates develop region-specific relationships the finding of decreasing accuracy with the increase of station density (Shen et al., 2020). Meanwhile, these lower-accuracy climate zones are generally located at the boundary of regions where some directions have no weather stations. We generated a global (50°S ~79°N) 1-km daily maximum and minimum Ta (i.e., Tmax and Tmin) dataset from 2003 to 2020 based on ground-based Ta measurements from weather stations and gap-filled LST dataset using the Spatially Varying Coefficient 270 Models with Sign Preservation (SVCM-SP) algorithm. The dataset showed acceptable accuracies based on the 10-fold cross-validation for five regions of the globe, compared to existing Ta datasets. The RMSEs of estimated Tmax and Tmin ranged from 1.20 to 2.44 and 1.69 to 2.39 ºC, respectively. The estimated Ta was affected by land cover types, elevation ranges, and climate types, with varying accuracies but within reasonable ranges. Our gridded Ta dataset effectively captured the spatial variation of Ta under clear physical meanings (i.e., negative and positive relationship with elevation and LST, respectively), which is not always 275 true in other gridded Ta datasets. The new dataset is unique in terms of spatiotemporal resolutions (i.e., 1-km daily maximum and minimum), global coverage, and temporal span and should be useful for a wide range of applications such as urban heat island phenomenon, hydrological modeling, and epidemic forecasting. However, the gridded Ta dataset may be limited by the performance of the SVCM-SP algorithm because only linear relationships between Ta with elevation and LST were used in the model. Therefore, future work can focus on improving the performance of the SVCM-SP algorithm by using more explanatory 280 variables under proper considerations of the relationship with Ta.