Recent asymmetric warming trends of daytime versus nighttime and their linkages with vegetation greenness in temperate China

Asymmetric warming has been increasingly discussed recently, yet knowledge of this difference in warming between daytime and nighttime is still limited. Most studies of how climate warming influences the terrestrial ecosystem often ignore this asymmetric effect. We investigated the change in temperature between daytime and nighttime and analyzed the relationships between normalized difference vegetation index and the temperature in the daytime (T_max) and the nighttime (T_min) from 1982 to 2015 in temperate China. Results showed a faster increase in T_min (0.46 °C dec⁻¹, p < 0.01) during the nighttime than in T_max (0.42 °C dec⁻¹, p < 0.01) during the daytime, which indicated an asymmetric warming rate. The asymmetric warming during the daytime and nighttime was closely related to variations in precipitation and solar radiation. The increasing T_min and T_max were most pronounced over a large portion of the entire temperate China, and their warming trends displayed a non-uniform spatial distribution. The area with daytime warming was larger than that with nighttime warming, approximately accounting for 99.53% and 96.22% of temperate China, respectively. The area with warming enhancing vegetation greenness was larger during the day (71.16% of temperate China, p < 0.05) than at night (61.60% of temperate China, p < 0.05), and vice versa, which presented asymmetric warming effects on China's temperate vegetation. We also found clear differences in the responses of the normalized difference vegetation index among different vegetation biomes to this asymmetric warming. Averagely, T_max was significantly related to the NDVI of shrub, desert, broadleaf forest, needleleaf forest, and swamp (p < 0.01). However, this similar relationship appeared only between T_min and desert vegetation (p < 0.01). Our findings emphasized the crucial role of asymmetric warming between the daytime maxima and nighttime minima in climate change research.