This study quantifies the spatial-temporal distribution of the near-surface temperature lapse rate (TLR) on the southeastern Tibetan Plateau (SETP) on the northern slopes of the eastern Himalayas, using the jackknife regression model. A 20-year (1985-2004) climatic data set of 16 stations between the elevation range of 3553-4801 m asl was used for this investigation. Controls for the spatial-temporal variation on TLRs and causes of higher mean square error (MSE) from the jackknife regressions were also analysed. Dry convection due to increasing sensible heating at lower elevations associated with clear skies, the effect of cold air surges, cloud/fog, as well as pronounced long-wave radiation loss due to snow cover at higher elevation, results in the super-dry adiabatic TLR in the dry winter. In response to the effects of high rainfall and humidity, intense latent heating at higher elevation due to moist adiabatic cooling causes TLRs to decrease significantly in summer. The variation in net radiation due to differences in moisture, rainfall, cloud cover and air mass between higher (fewer) and lower (higher) elevations further contributes to reducing TLRs values in this season. Observed steeper values of TLRs at higher elevations and more shallow values at lower elevations are due to the thermal contrast between snow-free ground and snow-covered mountain terrain, as well as the effect of variations in air mass and moisture. Based on derived values, this article also estimates near-surface temperature at higher elevations and analyses the precision of estimated values. Measurement of the lowest discrepancy (bias) between observed and estimated values from this study suggests that the estimation skills of the model are good enough. More precisely, including the smallest MSE from the jackknife regression and biases of the estimated values can be made for summer, perhaps due to the moisture controlled throughout the SETP, i.e. associated with the Indian monsoon. Higher MSE and biases are observed during winter and are due to the substantial effects of westerlies, as well as the station's geographical coordinate, local topography and microclimate. The MSE and the estimated biases are lowest at high-elevation stations and are associated with the lower contaminating effects of the surface. This estimation model, using derived values from this investigation, could be useful for glacier-hydro-climatic and ecological modelling in this region.