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The study of spatiotemporal variation in temperature is vital to assess changes in climate, especially in the Himalayan region, where the livelihoods of billions of people living downstream depends on water coming from the melting of snow and glacier ice. To this end, temperature trend analysis is carried out in the Narayani River basin, a major river basin of Nepal, characterized by three climatic regions: tropical, subtropical and alpine. Temperature data from six stations located within the basin were analyzed. The elevation of these stations ranges from 460 to 3800 m a.s.l. and the time period of available temperature data ranges from 1960–2015. Multiple regression and empirical mode decomposition (EMD) methods were applied to fill in missing data and to detect trends. Annual as well as seasonal trends were analyzed and a Mann–Kendall test was employed to test the statistical significance of detected trends. The results indicate significant cooling trends before 1970s, and warming trends after 1970s in the majority of the stations. The warming trends range from 0.028 to 0.035 <inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>°</mo></msup></semantics></math></inline-formula>C year<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> with a mean increasing trend of 0.03 <inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>°</mo></msup></semantics></math></inline-formula>C year<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> after 1971. Seasonal trends show the highest warming trends in the monsoon season, followed by winter and the premonsoon and postmonsoon season. However, the difference in warming rates between different seasons was not significant. An average temperature lapse rate of −0.006 <inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>°</mo></msup></semantics></math></inline-formula>C m<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> with the steepest value (−0.0064 <inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>°</mo></msup></semantics></math></inline-formula>C m<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>) in the premonsoon season and the least negative (−0.0052 <inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>°</mo></msup></semantics></math></inline-formula>C m<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>) in the winter season was observed for this basin. A comparative analysis of the gap-filled data with freely available global climate dataset show reasonable correlation, thus confirming the suitability of the gap filling methods.