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The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high‐quality near‐surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS  LST  products  for  a  long  period  of  time  (i.e.,  2002–present).  Specific machine learning methods were developed and linked with target‐oriented validation and then applied to convert LST  to Tair.  Spatial variables  in  retrieving Tair,  such  as  solar  radiation  and vegetation indices, were used  in estimation of Tair, whereas MODIS LST products were mainly  focused on temporal variation  in surface air  temperature. We validated our process using  independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell  in the range of 0.9–0.95 and 1–2  °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000–3000 m and 4000–5000 m, whereas the elevation interval at 6000–7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming.

Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau