###### Abstract
Experiments have shown that the latter is more important than the former [24; 25]. Changes in precipitation frequency were shown to notably affect plant growth and productivity by regulating run-off [26], soil moisture [27; 28], exposure to high radiation and temperature, and energy fluxes [22]. With global warming, extreme precipitation events have become more frequent than normal ones [29]; however, it remains unclear how precipitation frequency impacts vegetation productivity in different ecosystems within the context of climate change [30]. Furthermore, a clear seasonality has also been observed in the sensitivity of vegetation growth and development to precipitation frequency. For example, precipitation in autumn decreased while that in winter increased in recent decades [31; 32]. The precipitation occurring from January to July is the primary climatic factor causing fluctuations in the biomass production of plant communities [33; 34; 35]. However, it is not clear how precipitation frequency affects vegetation productivity during four different seasons because of the spatial heterogeneity of different land cover types or vegetation communities. Therefore, it is imperative to further analyze the spatial pattern of the precipitation-vegetation relationship while taking into account the potential impacts of precipitation frequency and distribution [36]. In studies focused on precipitation variability, precipitation intensity can be classified based on percentiles; for instance, heavy precipitation is typically defined as amounts surpassing the 95 th percentile [37; 38]. However, this classification presents limitations when applied to terrestrial ecosystem studies. First, it fails to adequately address ecological requirements: the water quantity essential for vegetation growth and the soil's effective moisture content remain constant, but relying on percentile classifications might overlook the true water demand of vegetation, leading to inaccuracies. Second, the variability aspect poses a concern: precipitation data for a consistent region can vary over time. For example, in the context of climate change, precipitation once labeled as \"heavy rain\" might currently be classified as moderate rain. Sole reliance on percentile-based division risks can overlook such shifts. In recent years, a number of studies have revealed that decreasing precipitation frequency contributes to early leaf expansion of the vegetation traits in northern ecosystems [25] and to the sensitivity of different vegetation communities to precipitation intensity [39]. However, most of these studies have ignored the interactions among season, precipitation frequency, precipitation intensity, and land cover types. Changes in precipitation frequency can directly affect vegetation and surface soil moisture. Increasedprecipitation frequency can wash away dust on the surface of leaves multiple times, thereby promoting vegetation photosynthesis [40]. The effects of the variation of precipitation on soil water content and vegetation growth are still unclear due to spatial heterogeneity, but it can be confirmed that precipitation frequency is more important for vegetation growth than precipitation intensity [25]. The gross primary production (GPP) of vegetation is the amount of carbon fixed during photosynthesis by all plants in the ecosystem. It is the first and most important flux of the terrestrial carbon cycle and was used in this study to represent the carbon sequestration capacity of vegetation. When analyzing the sensitivity of vegetation to climate change, GPP reflects variations in photosynthesis better than indicators such as the Normalized Difference Vegetation Index. Precipitation frequency and vegetation GPP exhibit a nonlinear relationship. Traditional regression analysis falls short in accurately and appropriately capturing the nuanced response of GPP to precipitation frequency. Consequently, the machine learning approach becomes an alternative choice.