## 5 Conclusions  
Precipitation frequency directly affects the fluctuation frequency of surface soil moisture, washes the leaves to improve photosynthesis, and regulates the temperature in the region, which has a large effect on vegetation growth. Based on the XGBoost model, this study analyzed the sensitivity of vegetation productivity to five precipitation frequencies at different precipitation intensities and in various land cover types in the Northern Hemisphere between 1982 and 2015. The results showed that vegetation growth was sensitive to precipitation frequency, and seasonal differences were revealed. Spatially, the higher the amount of precipitation, the smaller the area affected by precipitation frequency. Overall, during the study period, vegetation was less sensitive to the frequency of precipitation, with the largest downward trend for shrubs and the least fluctuation in barren land. Shrubland exhibited strong diversity of seasonal sensitivity. During the growing season, cropland, shrubland, and forests were more sensitive to the frequency of trace precipitation, while other land cover types were less sensitive. This may because heavy precipitation is needed for shrub growth in summer, and trace precipitation can hinder shrub growth. In regard to seasonal precipitation frequency in different land cover types, the results showed a time lag and differences in vegetation communities. Precipitation frequency did not crucially affect cropland growth during the growing season. Forest growth depended on heavy precipitation events during the cool season and small precipitation events during the warm season. The productivity of barren land species was significantly correlated with variations in precipitation frequency, but sensitivity did not vary significantly. These results can help fill the gap of considering only the amount of precipitation and ignoring the impact of frequency and seasonal changes on different land cover types. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16010021/s1](https://www.mdpi.com/article/10.3390/rs16010021/s1)]([https://www.mdpi.com/article/10.3390/rs16010021/s1](https://www.mdpi.com/article/10.3390/rs16010021/s1)), Figure S1: M-K trend of GPP values for each 0.25\({}^{\circ}\times\)0.25\({}^{\circ}\) grid during the four seasons between 1982 and 2015; Figure S2: Decade trend of GPP values during each season from 1982 to 2015 in every vegetation type; Figure S3: M-K trends of total precipitation frequency for each 0.25\({}^{\circ}\times\)0.25\({}^{\circ}\) grid across the four seasons from 1982 to 2015; Figure S4: Decade trends of precipitation frequency during each season from 1982 to 2015 in every vegetation type; Figure S5: Spatial distribution of the Pearson correlation coefficient between annual GPP values and total precipitation frequency from 1982 to 2015; Figure S6: Pearson correlation coefficients between annual GPP values and precipitation frequency across seasons in every vegetation type; Figure S7: Decade M-K trends of sensitivity to different precipitation frequencies during each season from 1982 to 2015 in every vegetation type; Figure S8: M-K trend of sensitivity to the frequency of very heavy precipitation from 1982 to 2015; Figure S9: Spatial distribution of the partial correlation coefficient between annual GPP values and total precipitation frequency from 1982 to 2015 when annual mean temperature was controlled. Software, S.X. ; supervision, S.X. and G.W. ; writing--original draft, S.X. and G.W. All authors have read and agreed to the published version of the manuscript. This study was funded by the National Key Research and Development Program of China (2022 YF0801302) and the National Natural Science Foundation of China (41930970 and 42077421).