## 3 Results
### Relationship between GPP Values and Precipitation Frequency  
The correlation coefficient between GPP and the frequency of seasonal precipitation mainly indicated a significantly negative relationship in most study areas, especially at northern latitudes, but a positive relationship at mid-latitudes (Figure 5). The seasonal contrasts showed that the change magnitude and areas with significance was smaller in winter than in the other seasons. Figure 6 shows the correlation coefficients between GPP and the seasonal precipitation frequency of different categories as well as the contribution of the variation in precipitation frequency to GPP values based on stepwise regression. Overall, the annual variation in precipitation frequency improved vegetation growth for most precipitation categories except trace precipitation (\"T\" column in the horizontal axis). Eight cases that significantly affected vegetation growth (40% of precipitation categories across seasons) were identified. These cases mainly include moderate precipitation during the growing season and heavy/very heavy precipitation in winter. Specifically, small precipitation in spring (\(p<0.05\)) and moderate precipitation (\(p<0.01\)) in summer and autumn improve  
Figure 4: Decade trends of precipitation frequency during each season from 1982 to 2015 in every vegetation type. The error (mean \(\pm\) 1.96 \(\times\) SE) bars represent 95% confidence intervals. vegetation growth. The frequency of trace precipitation and autumn GPP value seemed linked by a separate relationship, as the former nearly half of cases (64/120) showed a negative relationship with the latter across seasons. Figure 5: Spatial distribution of the Pearson correlation coefficient between annual GPP values and total precipitation frequency from 1982 to 2015. The red and blue colors in the lower left pie chart represent negatively correlated and positively correlated areas as a percentage of total area, respectively. The “+” symbol in the map indicates statistical significance at the 0.05 level. Figure 6: Pearson correlation coefficients between annual GPP values and precipitation frequency across seasons in every vegetation type. The letters T, S, S, A, and W in the horizontal ordinate stand for total year, spring, summer, autumn, and winter, respectively; P1–P5 indicate trace, small, moderate, heavy, and very heavy; * and ** in the T row indicate statistical significance at the 0.05 and 0.01 levels, respectively, obtained from the stepwise regression between precipitation frequency and GPP value from 1982 to 2015. Precipitation amounts ranging from small to very heavy promoted cropland growth in autumn, while trace amounts inhibited it in winter. In forests, small to moderate precipitation in spring, summer, and autumn was shown to play an important role (\(p<0.01\)), with correlation coefficients of 0.08, \(-0.23\), and \(-0.09\), respectively. However, Pearson correlation values were large for heavy, small, and very heavy precipitation in spring (0.19), summer (\(-0.23\)), and autumn (0.35), respectively. The variation in grassland GPP seemed to be promoted only by moderate and very heavy rain in summer (0.41-0.51), heavy precipitation during the growing season (P4-P5, \(p<0.05\)), and moderate precipitation in winter (\(p<0.01\)). It is notable that the variation in GPP values in grassland was less impacted by precipitation frequency in stepwise regression analysis compared to that in other land cover types. Heavy and very heavy precipitation during the growing season significantly affected (\(p<0.05\)) shrubland vegetation throughout the year. In barren land, lots of precipitation categories could promote vegetation growth, and only trace precipitation did not contribute significantly to it. Moderate to heavy precipitation during the growing season significantly contributed to GPP (\(p<0.05\)). The same was observed for the GPP ofundra vegetation, whose variation was controlled by heavy and very heavy precipitation in winter (\(p<0.01\)).