## 2 Materials and Methods  
### Meteorological and CO2 Flux Data Sets  
To analyze the possible CO2 flux feedbacks of tropical terrestrial ecosystems on the extreme temperature and precipitation events, 17 monitoring stations of CO2 fluxes situated on different continents in tropical latitudes with various landscape and climate conditions and providing the open access data were selected. All stations were assigned to 4 main biome types [36] in accordance with ecosystem classification used in the FLUXNET archive: savunas and woody savannas, evergreen and tropical rain forests, dry or seasonal forests (include deciduous needle-leaf forests and tropical seasonal deciduous forests), and wetlands (Figure 1).  
Figure 1: Location of the monitoring stations with the type of biome indicated by the color (see the map legend) and the available period of observations (indicated in the brackets).  
To identify the extreme weather conditions, the reanalysis produced by the European center for medium-range weather forecast ERA5 [37] was used. We used the temperature at 2 m above ground with a temporal resolution of 3 h and precipitation amount with an hourly resolution. The spatial resolution of the data sets was 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), and the period analyzed was 1991-2021. We believed that the reanalysis data set was more useful for our study, as they did not have gaps and were, therefore, able to provide a continuity of time-series data and, therefore, better data comparability at different locations. The FLUXNET meteorological data set contained numerous gaps in temperature and precipitation data, ranging from 2 to 37% for selected stations. This made their application for spatial and temporal data analysis challenging. A correlation analysis revealed a strong agreement between the reanalysis and the gap-free FLUXNET data sets for the air temperature. The R-squared values for the temperature data sets ranged mainly from 0.69 (AU-Fog) to 0.96 (AU-Rob) at \(p<0.05\) (Supplementary Materials Figure S1). For MY-PSO and Br-Sa1 (Figure S2), it was somewhat less (0.69 at \(p<0.05\)). The agreement between the precipitation rates obtained from reanalysis and monitoring stations was worse due to the high spatio-temporal inhomogeneity of the precipitation fields and the numerous gaps in precipitation measurements at the flux stations. At the same time, it was noteworthy that the correlation between extreme precipitation (exceeding the thresholds) from reanalysis and FLUXNET data for the threshold 95% quantile at stations with a longer period of observation (more than 6 years) remained high, ranging from 0.45 to 0.73 at \(p<0.05\) (Figure S3). The latter factor determined the appropriateness of reanalysis precipitation data in our study.  
Information about net daily CO\({}_{2}\) fluxes (NEE) was taken from the FLUXNET archive [24] that had open access to historical flux data sets. Available datasets covered the periods from 1 to 9 years. The archive included meteorological data, net radiation, CO\({}_{2}\) fluxes, and latent and sensible heat fluxes measured using the eddy covariance method [38]. The eddy covariance method allowed for providing direct long-term measurements of turbulent GHG fluxes on an ecosystem scale. All FLUXNET stations used similar standardized equipment and data processing software to produce comparable time-series data on GHG fluxes. The gaps in the CO\({}_{2}\) flux time series, caused by equipment and power failures, low wind, strong rainfalls, etc., for all selected flux stations were filled using the REddyProc package [39].