## 2 Materials and Methods
### Data Analysis  
The gridded SPEI data for spring (March, April, and May (MAM); SPEI-3 for May month) and summer (June, July, and August (JJA); SPI-3 for August month) were obtained from the SPEI GlobalDrought Monitor ([[http://spei.csic.es/index.html](http://spei.csic.es/index.html)]([http://spei.csic.es/index.html](http://spei.csic.es/index.html))). The drought areas were defined as the areas which showed SPEI less than or equal to \(-1\) (meteorological drought conditions) for both spring and summer season [52; 53] of 2018, whereas the non-drought areas were the ones with SPEI greater than \(-1\) (near normal and wet conditions, or non-drought conditions) for both spring and summer of 2018.  
Standardized meteorological-based drought indices can sometimes misrepresent actual climatic water balance (CWB) [54]. The SPEI values of less than \(-1\) for the study area in 2018 completely indicated water deficit conditions with negative CWB values during the summer season (Figure S3). Figure 1c shows the drought and non-drought area used in this study. Thereafter, the OCO-2 SIF sounding data for the drought and non-drought areas were spatially joined to the CLC based on geographic intersection (intersection of SIF soundings and the CLC polygons). To study the variation of SIF across the drought and non-drought areas in spring and summer for Europe, we spatially aggregated all the daily SIF soundings for each of the above described vegetation types separately for drought and non-drought areas over the spring and summer season.  
SIF changes because of two reasons--firstly due to natural seasonal vegetation change during the growing season (e.g., change in chlorophyll content during the seasonal cycle) and secondly due to stress (e.g., heat and drought stress). To study the inter- and intra-seasonal SIF variation and anomalies during the drought in spring and summer, we compared the SIF, temperature, and VPD of 2018 (i.e., SIF_2018, Temp_2018, VPD_2018) with their baseline value (mean of 2015-2017, i.e., SIF_Mean, Temp_Mean, VPD_Mean) on a 5-day running average [9]. These anomalies of the average SIF at a given date would eliminate the SIF changes due to seasonality. It is important to note that Central Europe suffered from a heat wave during summer of 2015, with record high temperatures and a precipitation deficit [55; 56], which may lower our detected impact of 2018. For each OCO-2 footprint, co-located MODIS's NDVI and FPAR were interpolated in time and space so that the same sample is used for each dataset. Finally, the OCO-2 SIF was aggregated spatially (i.e., drought area) and temporally (i.e., 8-day) for it to be compared with NDVI and fPAR.  
We performed a Wilcoxon-rank sum test [57] to test for a significant location shift (i.e., the nonparametric difference of means) between the SIF_2018 and SIF_Mean. SIF, temperature, and VPD anomalies (i.e., SIF-diff, Temp-diff, and VPD-diff) were defined as differences between 2018 and average (e.g., SIF-diff = SIF_2018-SIF_Mean). With continued drought condition during the summer, Central Europe also experienced an intense heatwave during late July to early August 2018. The heatwave was pronounced in Belgium (BEL), Netherlands (NLD), Denmark (DEN), and Germany (DEU), according to the reports published by the Royal Meteorological Institute of Belgium [58], the Royal Netherlands Meteorological Institute [59], the Danish Meteorological Institute [60], and the German Meteorological Office [61], respectively. To quantify the impact of this heatwave, SIF soundings of BEL, NLD, DEN, and DEU from 15 July to 15 August 2018 were analyzed for all the vegetation types and compared to the mean of 2015-2017. All analyses were processed in R [62] and extended by the packages stats, ggplot2 [63] and dplyr [64].