###### Abstract  
Droughts that occur in tropical forests (TF) are expected to significantly impact the gross primary production (GPP) and the capacity of carbon sinks. Therefore, it is crucial to evaluate and analyze the sensitivities of TF-GPP to the characteristics of drought events for understanding global climate change. In this study, the standardized precipitation index (SPI) was used to define the drought intensity. Then, the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) was utilized to simulate the dynamic process of GPP corresponding to multi-gradient drought scenarios--rain and dry seasons \(\times\) 12 level durations \(\times\) 4 level intensities. The results showed that drought events in the dry season have a significantly greater impact on TF-GPP than drought events in the rainy season, especially short-duration drought events. Furthermore, the impact of drought events in the rainy season is mainly manifested in long-duration droughts. Due to abundant rainfall in the rainy season, only extreme drought events caused a significant reduction in GPP, while the lack of water in the dry season caused significant impacts due to light drought. Effective precipitation and soil moisture stock in the rainy season are the most important support for the tropical forest dry season to resist extreme drought events in the study area. Further water deficit may render the tropical forest ecosystem more sensitive to drought events. t 2022 14 157 10.3390/w14020157 10.390/w14020157 10.  
increased the frequency and intensity of ecological droughts [13]. In the future, precipitation in subtropical and tropical regions is expected to decrease [13], and tropical forests are highly susceptible to drought disturbances [14]. In order to improve the carbon sink capacity of tropical forests, the topic of how to reduce the impact of drought on tropical forests has become a major point of focus that is currently being faced. Existing research on drought-affected tropical forests mainly focuses on the Amazon rainforest [8; 10; 12; 15; 16]. The drought response of tropical forest ecosystems is analyzed by means of field observations, sample plot control experiments, and large-scale remote sensing inversion [8; 17; 18; 19]. However, on-site experiments require long-term monitoring and consume a great deal of manpower and material resources [20; 21]. Moreover, the tropical forest structure is highly complex, climate change amplitude is difficult to predict [22], tropical areas are covered with clouds throughout the year, and remote sensing methods are limited [23; 24; 25]. Therefore, models have become an important tool for observing and understanding the response of tropical forests to drought. Compared with Amazon and African tropical forests, tropical forests in Asia have the highest bio-carbon storage [26] and are in the Western Pacific, which is severely affected by El Nino-Southern Oscillation (ENSO) events. The tropical forests of Xishuangbanna in China are located on the northern edge of tropical southwest Asia and form part of the Malay rainforest in India. Affected by the tropical monsoon climate, precipitation presents seasonal changes, and the dry and rainy seasons are distinct. Furthermore, it is a natural site for conducting research on the response of tropical forest ecosystems to drought. The sensitivity of the TF-GPP response to multiple drought scenarios in this region is an important supplement to the global tropical forest GPP affected by drought. The formation of drought events is extremely complex. In recent years, various drought indices have been used to evaluate different drought parameters to quantitatively analyze drought events [5]. Tropical forest ecosystems are more sensitive to moisture [27; 28]. Compared with other drought indices such as the Palmer drought severity index (PDSI), the standardized precipitation index (SPI) is more suitable for tracking tropical forest drought events [29; 30].