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• Cumulative and time-lag effects combined impact vegetation activities. • Solar radiation and temperature are the dominant climate factors in taiga and tundra. • The duration of temporal effects corresponds with ecosystem complexity. • The tundra converted to forests caused by warming enhanced the climate temporal effects. The Northern Hemisphere is experiencing amplified warming and greening; however, the responses of greening to warming remain unclear in terms of the cumulative and time-lag effects of climate factors. We chose Siberia, a hotspot in the Northern Hemisphere, as a study area and identified the relationships between the enhanced vegetation index (EVI) and climate factors (e.g., surface temperature, precipitation, and solar radiation) during 2000–2016. In the growing season, the average cumulative duration of the vegetation response to climate factors was 3.6 ± 2.0 months, and the time-lag period was 1.5 ± 1.2 months, with the longest duration in forests and the shortest duration in tundra. Solar radiation was the dominant driver of vegetation greening in 56% of the study area, followed by temperature (41%) and precipitation (3%). In addition, the temporal effects of the climate factors were separated through partial correlation analysis; vegetation responded most rapidly to solar radiation variations with a cumulative duration of 2.2 ± 1.2 months and a time-lag period of 1.8 ± 0.8 months, followed by temperature (2.3 ± 1.3 and 1.9 ± 1.0 months, respectively). Precipitation showed the longest cumulative effect (3.7 ± 1.5 months) and the shortest time-lag effect (1.2 ± 1.1 months). Our results suggest that vegetation activity is influenced by both cumulative and time-lag effects. The temporal effects vary among the land cover categories, and complex ecosystems generally correspond to long-term temporal effects, and vice versa. Thus, the conversion of tundra to boreal forest due to warming in Siberia enhances the temporal effects of climate. These temporal effects could be incorporated into Earth system models to improve our understanding and monitoring of terrestrial ecosystems. [ABSTRACT FROM AUTHOR]