Spatially varying relevance of hydrometeorological hazards for vegetation productivity extremes

Vegetation plays a vital role in the Earth system by sequestering carbon, producing food and oxygen, and providing evaporative cooling. Vegetation productivity extremes have multi-faceted implications, for example on crop yields or the atmospheric CO2 concentration. Here, we focus on productivity extremes as possible impacts of coinciding, potentially extreme hydrometeorological anomalies. Using monthly global satellite-based Sun-induced chlorophyll fluorescence data as 15 a proxy for vegetation productivity from 2007 2015, we show that vegetation productivity extremes are related to hydrometeorological hazards as characterized through ERA5-Land reanalysis data in approximately 50% of our global study area. For the latter, we are considering sufficiently vegetated and cloud-free regions; and we refer to hydrometeorological hazards as water or energy related extremes inducing productivity extremes. The relevance of the different hazard types varies in space; temperature-related hazards dominate at higher latitudes with cold spells contributing to productivity minima and 20 heat waves supporting productivity maxima, while water-related hazards are relevant in the (sub)tropics with droughts being associated with productivity minima and wet spells with the maxima. Next to single hazards also compound events such as joint droughts and heat waves or joint wet and cold spells play a role, particularly in dry and hot regions. Further, we detect regions where energy control transitions to water control between maxima and minima of vegetation productivity. Therefore, these areas represent hot spots of land-atmosphere coupling where vegetation efficiently translates soil moisture dynamics into 25 surface fluxes such that the land affects near-surface weather. Overall, our results contribute to pinpoint how potential future changes in temperature and precipitation could propagate to shifting vegetation productivity extremes and related ecosystem services.

hydrometeorological hazards as characterized through ERA5-Land reanalysis data in approximately 50% of our global study area. For the latter, we are considering sufficiently vegetated and cloud-free regions; and we refer to hydrometeorological hazards as water or energy related extremes inducing productivity extremes. The relevance of the different hazard types varies in space; temperature-related hazards dominate at higher latitudes with cold spells contributing to productivity minima and 20 heat waves supporting productivity maxima, while water-related hazards are relevant in the (sub)tropics with droughts being associated with productivity minima and wet spells with the maxima. Next to single hazards also compound events such as joint droughts and heat waves or joint wet and cold spells play a role, particularly in dry and hot regions. Further, we detect regions where energy control transitions to water control between maxima and minima of vegetation productivity. Therefore, these areas represent hot spots of land-atmosphere coupling where vegetation efficiently translates soil moisture dynamics into 25 surface fluxes such that the land affects near-surface weather. Overall, our results contribute to pinpoint how potential future changes in temperature and precipitation could propagate to shifting vegetation productivity extremes and related ecosystem services.

Introduction
Vegetation is a crucial component of the Earth system because it provides ecosystem services like food and oxygen production, 30 CO2 sequestration and evaporative cooling. Therefore, the effects of changes in vegetation productivity are diverse; it influences crop yields , cloud formation (Hong et al., 1995;Freedman et al., 2001), precipitation (Pielke Sr et al., 2007, atmospheric pollution (Otu-Larbi et al., 2019) and heat wave intensity (Li et al., 2021b).
Photosynthesis requires sufficient water (soil moisture) and energy (incoming shortwave radiation) supply. In regions that are water (energy) limited, plants usually benefit from water (energy) surpluses and suffer from respective deficits. Many studies 35 confirm that, depending on the evaporative regime, vegetation productivity follows the temporal evolution of influential variables such as soil moisture or temperature which summarize the water or energy dynamics (Beer et al., 2010;Seddon et al., 2016;Madani et al., 2017;Denissen et al., 2020;Piao et al., 2020;Li et al., 2021a).
Correspondingly, hydrometeorological hazards, such as temperature and precipitation extremes have implications on vegetation productivity. Many studies investigated the influence of such hazards on vegetation productivity, highlighting their 40 impact on the biosphere (Ciais et al., 2005;Zhao et al., 2010;Zscheischler et al., 2013;Zscheischler et al., 2014a;Zscheischler et al., 2014b;Flach et al., 2018;Wang et al., 2019;Zhang et al., 2019;Qui et al., 2020). However, usually these studies focus on particular types of hydrometeorological hazards such as droughts or heat waves, or they use vegetation productivity data from models or other proxies rather than the recent satellite-derived Sun-induced chlorophyll fluorescence (SIF) data (Frankenberg et al., 2011;Joiner et al., 2013). 45 In this study, we re-visit the relationship between vegetation productivity and hydrometeorological hazards by analyzing the implications of both single and compound hazards on vegetation productivity extremes, as has been highlighted before (Sun et al., 2015, Zhou et al., 2019. However, to our knowledge for the first time, we do so comprehensively by approximating variable importance during vegetation productivity extremes inferred from SIF data on a global scale. This analysis is done from an impact perspective; we first detect impacts (productivity extremes) before relating them to coinciding, potentially 50 extreme hydrometeorological anomalies (Smith, 2011). Finally, we investigate where the full vegetation productivity range between minima and maxima involves transitions from energy to water controls. In regions where this occurs, the feedback of the land surface on the climate can be stronger, as the water-controlled vegetation translates soil moisture dynamics through its energy and water fluxes to affect the boundary layer and consequently also near-surface weather. Hence, our vegetationbased analysis can indicate hot spots of land-atmosphere coupling (Koster et al., 2004;Guo and Dirmeyer, 2013). 55 In section 3.1 we investigate the co-occurrence of vegetation productivity extremes and hydrometeorological hazards. Further, we show the timing of such vegetation productivity extremes in section 3.2. Additionally, we determine the main drivers of vegetation productivity extremes and assess the influence of underlying evaporative regimes in section 3.3. We summarize our results across climate regimes in section 3.4 and investigate regions with vegetation productivity controls switching between water and energy variables in section 3.5. 60

Data and methods
In order to characterize vegetation behavior, we use SIF and Enhanced Vegetation Index (EVI) data in this study. SIF is used as a proxy for vegetation productivity. We employ satellite-observed SIF data retrieved from the Global Ozone Measurement Experiment (GOME-2;Koehler et al., 2015). In the derivation of this SIF product, multiple corrections for varying solar zenith angles, differences in overpass times and cloud fraction have been applied to yield reliable SIF estimates. In addition to 65 vegetation productivity, we also study changes related to vegetation greenness by using satellite-observed EVI data from Moderate-resolution Imaging Spectroradiometer (MODIS; Didan, 2015).
As for the hydrometeorological variables, representing energy and water availability, we consider 2m temperature, shortwave incoming radiation, vapor pressure deficit, soil moisture from 4 layers (1: 0-7 cm, 2: 7-28 cm, 3: 28-100 cm, 4: 100-289 cm) and total precipitation, all from the ERA5-Land reanalysis data (Muñoz-Sabater, 2019). In addition to this, and to validate the 70 robustness of our results, we use an alternative soil moisture product, SoMo.ml, which provides data for three layers (1: 0-10 cm, 2: 10-30cm, 3: 30-50cm), and which is derived through a machine learning approach that is trained with in-situ soil moisture measurements from across the globe (O and Orth, 2021). All datasets used in this study are summarized in Table 1.
The workflow applied to these datasets is illustrated in Fig. 1. At first, all data is pre-processed for comparability by (i) aggregating it to monthly, half-degree spatial and temporal resolution and by (ii) focusing on the time period 2007-2015. Next,75 we compute anomalies by removing linear trends and the mean seasonal cycle from the data for both the vegetation and hydrometeorological variables. In each grid cell, we disregard months with an absolute SIF value below 0.5 mW/m2/sr/nm to focus on times with sufficiently active vegetation (as in Li et al., 2021a). Additionally, grid cells with a fractional vegetation cover < 5% are excluded from the analysis. Finally, we assure the necessary data availability by considering only grid cells with > 15 monthly anomalies across the study period remaining after the filtering. Out of the identified suitable months in each 80 grid cell, we determine the five strongest negative and five strongest positive monthly SIF anomalies. The sum of all grid cells for which five SIF maxima and minima can be detected is referred to as total study area.
After this filtering, we follow two approaches in our analysis. In the first approach, we check for hydrometeorological hazards coinciding with the determined extreme vegetation productivity events. Thereby, we consider air temperature and soil moisture layer 2 as these variables were previously found to be globally most relevant for vegetation productivity (Li et al., 2021a). At 85 first, we average the monthly temperature and soil moisture anomalies across the five months of maximum and minimum SIF anomalies. Then, a series of steps is taken to test if the coinciding hydrometeorological anomalies during SIF extremes are actually hazardous: (i) We randomly sample five months with sufficiently active vegetation and average the soil moisture and temperature anomalies, respectively, across them. (ii) We repeat this 100 times to obtain a distribution from which we determine the 10th and 90th percentile. (iii) A hydrometeorological hazard is detected if the actual, averaged temperature 90 and/or soil moisture anomalies associated with the SIF extremes are below 10th (cold spell or drought) or above the 90th percentile (heat wave or wet spell) of the distribution of randomly sampled averaged anomalies. Note that with this approach we can detect both single and compound hydrometeorological hazards.
Complementing this analysis, in the second approach we analyze the temporal co-variation between SIF extremes and hydrometeorological anomalies. For this purpose, we correlate the five SIF extreme anomalies with anomalies of all considered 95 hydrometeorological variables in each grid cell. We include respective SIF and hydrometeorological data from the surrounding grid cells to yield a larger data sample consisting of 5 x (8+1) = 45 data pairs. We disregard negative and insignificant (p-value > 0.05) correlations, as we assume these are not indicating actual physical controls but rather represent the influence of noise or confounding effects such as low precipitation during times of high radiation. This also serves to deal with uncertainty in the SIF data set. When systematic patterns emerge from either of the approaches with adequate significance, they are unlikely 100 confounded by underlying SIF patterns: as we focus solely on either SIF maxima or minima, statistically significant relations only emerge when concurrent hydrometeorological anomalies of an appropriate magnitude exist. Finally, the hydrometeorological variable that yields the highest correlation coefficient with the extreme SIF anomalies is regarded as the main SIF-controlling variable during vegetation productivity maxima or minima.
3 Results and discussion 105 Figure 2 shows which hydrometeorological hazards are associated with SIF extremes as inferred with approach 1 described in Section 2 and in Fig. 1. In approximately 50% of the global study area, we find that vegetation productivity extremes are associated with hydrometeorological hazards. This is in line with previous research (Zscheischler et al., 2014b). For both maximum and minimum vegetation productivity, we find spatially coherent patterns of associated hydrometeorological 110 hazards. In the Northern Hemisphere SIF maxima (minima) at high latitudes relate to heat waves (cold spells), where in mid latitudes they occur jointly with wet spells (droughts). This suggests that hydrometeorological hazards associated with SIF extremes vary systematically according to energy-and water control of the local vegetation. Thereby, the boundary between both regimes and the respectively determined relevant hydrometeorological hazards is surprisingly sharp, for example in North America, and in eastern Europe and Russia (Flach et al., 2018). 115

Hydrometeorological hazards and vegetation productivity extremes
Further, single hydrometeorological hazards (either an extreme temperature or soil moisture anomaly) are relevant in more areas than compound hazards (combination of extreme temperature and extreme soil moisture anomaly). Compound hazards seem to be particularly important in the sub-tropics on both hemispheres. Differences also exist between maximum and minimum vegetation productivity extremes, the latter being slightly more associated with compound hazards.
Overall, the most frequent hazards during vegetation productivity minima are droughts and cold spells. Previous studies have 120 reported the relevance of drought in this context (Zscheischler et al., 2013;Zscheischler et al., 2014a;Zscheischler et al., 2014b) even though for different vegetation productivity proxies. On the contrary, the importance of cold spells is not analyzed, probably because vegetation productivity in boreal regions is comparably smaller than in e. g. tropical regions (Li and Xiao, 2019).
The results in Fig. 2 are based on averages of the five months with strongest SIF anomalies in each grid cell. Figure S1 shows 125 co-occurring hydrometeorological hazards separately for each of the five SIF maxima and minima. The patterns are similar as in Fig. 2, we consistently find temperature-related hazards to be relevant in energy-controlled regions and water-related hazards in water-controlled regions across all five individual SIF extremes. Weaker SIF extremes tend to be less associated with hydrometeorological hazards. This could be because the signal-to-noise ratio is decreased for weaker extremes, or other factors such as disturbances (fire or insect outbreaks) play a more prominent role for these productivity extremes. As mentioned, soil 130 moisture layer 2 is used here to detect droughts and wet spells, but similar results are obtained with soil moisture layers 1 and 3, respectively (not shown).

Timing of strongest SIF extreme
To further understand the spatially varying relevance of hydrometeorological hazards, we show the months of the year associated with the strongest SIF extreme in each grid cell in Fig. 3. The spatial pattern is quite different from that in Fig. 2,  135 for example the sharp transitions between regions with energy and water-related hydrometeorological hazards are not present in Fig. 3. Hence, this transition is apparently not related to SIF extremes occurring in different seasons and might be rather related to different evaporative regimes which will be further investigated in the next subsection 3.3. The spatial variability in Fig. 3 is lower at high latitudes compared with (sub-)tropical regions. At high latitudes the growing season is short and constrained by energy availability. In the tropics, we find an increased smaller-scale variability, presumably due to the weak 140 seasonal cycle of hydrometeorological variables. Most SIF extremes in North America and Eurasia occur in the early growing season, presumably when vegetation either starts to grow or growing is limited due to energy or water control. While here we show the months-of-year associated with the strongest SIF extreme, in Fig. S2 we show similar patterns in the timing of the 2nd to 5th strongest SIF extremes, indicating that each of the remaining SIF extremes occurs in similar months-of-year.

Hydrometeorological drivers of vegetation productivity extremes 145
After showing the co-occurrence of hydrometeorological hazards with SIF extremes, we apply a correlation analysis (approach 2 in section 2) to characterize the co-variability between extreme SIF anomalies and concurrent hydrometeorological anomalies. Figure 4 shows the hydrometeorological variable that correlates strongest with SIF during extreme vegetation productivity months, indicating respective controls. At the high latitudes and in the tropics SIF extremes are generally energy controlled, while in the mid latitudes and subtropics they are water controlled. Overall, we find similar spatial patterns as in 150 Fig. 2, demonstrating consistent results across co-occurrence and co-variability of SIF extremes and hydrometeorological hazards. This coherence suggests that hydrometeorological hazards play a key role in inducing SIF extremes.
The bar plot insets in Fig. 3 indicate that SIF maxima are equally controlled by energy and water variables while SIF minima are overall more water controlled. Even though weaker, this shift is also present in Fig. 2. This difference can be explained with transitional regions, which have energy-controlled SIF maxima, but water-controlled SIF minima. This is illustrated for 155 example by the northward shift of the transition between energy and water control in Russia when comparing the results for maximum and minimum SIF. These transitional regions will be further investigated in section 3.5.
We repeated this analysis with SoMo.ml soil moisture and found similar spatial patterns of energy-and water-controlled regions (Fig. S3), underlining that our results are robust with respect to the choice of the soil moisture product. Furthermore, we repeat our co-variability analysis for EVI instead of SIF in Fig. S4, which allows us to contrast to some extent the behavior 160 of vegetation physiology (SIF) and vegetation structure (EVI). Similar to the spatial patterns of energy-and water-controlled vegetation in Fig. 4, EVI shows predominant energy control at high latitudes, while the mid latitudes are largely watercontrolled. Further, as in Fig. 4 for SIF, EVI minima are more associated with water variables than EVI maxima. However, the overall extent of water-controlled areas is clearly larger in the case of EVI compared with the SIF results. This could (i) partly be related to the fact that EVI, being less dynamic than SIF because it is more related to vegetation greenness 165 and structure, tends to vary at time scales more in line with that of soil moisture (Turner et al., 2020), which can support stronger correlations. Or (ii) it could be due to confounding effects of the changing soil/vegetation color between dry and wet states on the EVI signal.

Hydrometeorological controls across climate regimes
In addition to analyzing the spatial variation of the main drivers of vegetation productivity extremes, we attempt to further 170 understand the large-scale patterns along temperature and aridity gradients. To this end, we bin grid cells by their climate characteristics as denoted by long-term mean temperature and aridity (the ratio between unit-adjusted net radiation and precipitation). The results in Fig. 5 illustrate which hydrometeorological variable most often has the highest correlation with SIF anomalies in each climate regime. controlled, with temperature controlling in cold regions (long-term average temperature < 10 °C) and radiation controlling in warm regions (long-term temperature > 10 °C). In contrast, productivity extremes in arid regions (aridity > 2, Budyko, 1974) are mainly water controlled, with soil moisture layer 2 and 3 as most important water controls. The main difference between maximum and minimum SIF results is detectable in semi-arid regions (1 < aridity < 2). While for maximum SIF those climate regimes show mostly energy control, SIF minima in these regimes are largely water controlled. From this, we deduce that 180 semi-arid regions represent the transitional regime, as the main drivers change from energy to water variables from SIF maximum to SIF minimum. Fig. S5 indicates that hydrometeorological anomalies do not solely elicit immediate, but also lagged vegetation responses. A clear difference between water-and energy-controlled conditions is already visible when correlating hydrometeorological anomalies of the preceding month with the respective SIF extreme. Energy and water surpluses and deficits establish over 185 time, which is most clearly evidenced in arid regions, where precipitation and shallow soil moisture of the preceding month is found to be the most important variable. With time, deeper soil moisture becomes more important (Fig. 5a-b), as in case of SIF maxima, precipitation needs time to infiltrate the soil and in case of SIF minima, the soil dries most rapidly from the top down.
The results for EVI show similar patterns despite an increased overall water control as seen earlier in the global maps (Fig.  190   S4). For example, where in humid regions SIF extremes are mainly energy controlled, EVI extremes are more often water controlled, which is also reflected in the global maps in Fig. S4. Fig. S6 illustrates similar controlling hydro-meteorological variables for SIF and evapotranspiration (ET) extremes. This suggests that carbon and water cycles are sensitive to similar hazards, which in turn enhances their impact on the land climate system via both carbon and water pathways. This further demonstrates the usefulness of SIF observations for reflecting plant 195 transpiration (Jonard et al., 2020). Further, Fig. S6 shows that GLEAM ET extremes relate much more strongly to surface soil moisture than GOME-2 SIF extremes. This could be due to the part of ET that partitions into an unproductive part, bare soil evaporation, which evaporates water from the surface layer directly and a productive part, which is connected to carbon uptake and therefore SIF. Surface soil moisture affects the unproductive part, while overall enhancing the role of surface soil moisture for ET. 200 Figure 5 (e) and (f) show the results of Fig. 2 binned according to their long-term climate characteristics. In humid regions, both SIF extremes are co-occurring with temperature hazards. In contrast, in arid regions water-related hazards co-occur with maximum and minimum SIF. Thereby, Fig. 5 underlines once more the similarity of the results obtained with approaches 1 (Fig. 2) and 2 (Fig. 4).
To additionally explore the influence of different vegetation types and their respective plant physiological differences on the 205 main controls of vegetation productivity, we bin the grid cell results by the respective fraction of tree cover of the entire vegetation cover, and by aridity in Fig. S7. We find that the radiation control of SIF extremes in humid regions is mostly associated with forests, and that the water control in semi-arid regions largely occurs for shorter vegetation, with presumably more shallow root systems, while productivity extremes in more forested semi-arid regions tend to be energy-controlled. IN very strong droughts, tall trees with deep rooting systems are particularly prone to suffer hydraulic failure (Brum et al., 2019). 210 However, in our analysis we consider 5 events in a 15-year time period, such that we likely don't exclusively capture very strong droughts that might results in tree mortality. Generally, hardly any changes in the most important variables can be seen with variations in tree cover, suggesting that on a global scale plant physiological differences only have a limited effect on determining the most important control for SIF extremes. As in Fig. 5, similar patterns are found for EVI extremes with overall increased relevance of water variables particularly in short vegetation-dominated regions. 215

Switching hydrometeorological controls between SIF maxima and minima
In a final step, we focus on shifts between energy and water control when moving from SIF maxima to SIF minima. The respective transitional regions represent hot spots of land-atmosphere coupling as (i) in these regions the land surface (soil moisture) is affecting near-surface weather at least during productivity minima (therefore also influencing transpiration) and (ii) this effect can be significant as transpiration (variability) is relatively high compared with drier regions where vegetation 220 productivity would be water-limited across its entire range from minimum to maximum. The results are depicted in Fig. 6, which illustrates these emerging transitions from water to energy control (yellow) and vice-versa (blue, denoting landatmosphere hot spots). Grid cells that stay within water or energy control, even with a change between the water or energy variables, respectively, are shown in black indicating no transition. Figure 6 (a) reveals many regions with no transition.
Transitions are found mostly in North Eurasia and North America. Globally, a change from energy control during maximum 225 SIF to water control during minimum SIF occurs more often (7% of the study area) than the opposite transition (4%). Figure 6b and c display the percentage of grid cells in each climate regime changing from water to energy control and viceversa with grid cells binned with respect to long-term climate conditions, similar to Fig. 5. The highest fraction of grid cells in each climate regime would show no change, but as we focus on transitioning grid cells, only they are displayed. Transitions from water to energy control between SIF maxima and SIF minima happen most often in cold, humid regions. This is deviating 230 from the prevailing energy control in these climate regimes, and probably related to local-scale features and/or micrometeorological conditions. Figure 6 (c) indicates that changes from energy control during maximum SIF to water control during minimum SIF most frequently occur in the semi-arid transitional regions. These are land-atmosphere coupling hot spots as described above. The transition from energy to water limitation could be caused by energy-controlled maxima in spring, when presumably soil water resources are available after being replenished during autumn and winter. With sufficient water 235 supply, energy surpluses could induce vegetation productivity maxima. During summer, soil moisture could be depleted for example by the high vegetation demand, and therefore taking over the SIF control of photosynthesis that is reflected into the SIF dynamics.

Limitations
Our results are obtained at, and valid for, relatively large spatial (half degree) and temporal (monthly) scales. Previous studies 240 have shown differences in the vegetation-climate coupling across scales (Linscheid et al., 2020), suggesting it would be worthwhile to repeat our analysis for different spatiotemporal scales in the future, possibly with new satellite data products. In this context it should be noted, however, that while the relationship between SIF and gross primary productivity (GPP) as actual vegetation productivity is strong for large spatio-temporal scales (Frankenberg et al., 2011;Guanter et al., 2012;Joiner et al., 2013), it can deteriorate towards smaller scales (He et al., 2020;Maguire et al., 2020;Marrs et al., 2020;Wohlfahrt et 245 al., 2018). And the spatiotemporal range within which there is an acceptable SIF-GPP relationship is not entirely clear yet.
As a second source of uncertainty, SIF data with their relatively large spatial footprint are more vulnerable to cloud contamination compared to finer-scale satellite products (Joiner et al., 2013). Also, especially across South America the SIF data quality is decreased to additional noise (Joiner et al., 2013;Köhler et al., 2015). In our study, many grid cells in these regions and other tropical, cloud-dominated regions exhibit insignificant or negative correlations between SIF and 250 hydrometeorological anomalies, which is why no hydrometeorological controls can be determined there (Fig. 4). Confirming the validity of our results for the tropical grid cells where results can be obtained, we find mostly consistent and physically meaningful results, e. g. radiation being a main driver of vegetation productivity as the cloud cover is limiting radiation (reported similarly for non-extreme conditions by Green et al., 2020 andLi et al., 2021a).
Next to the SIF data, there is also noteworthy uncertainty in the soil moisture data from ERA5. While data quality of surface 255 soil moisture benefits from (satellite) data assimilation, the soil moisture dynamics in deeper layers are more model-based which is somewhat contradicting the observational character of our study. Therefore, we use soil moisture data from SoMo.ml as an independent data set, which is not based on physical modelling and the related assumptions and parameterizations as it is derived with machine learning applied to in situ measurements from different depths. Overall, the similar results obtained with ERA5-Land and SoMo.ml soil moisture confirm the robustness of our results despite uncertainties in the soil moisture 260 data.
Finally, the use of correlation methods for inferring causal relations is potentially insufficient and under debate (Krich et al., 2020). We want to emphasize that in our study when referring to "drivers" or "controls" of vegetation productivity, we simply base this on correlation and do not imply causality. Nevertheless, we try to filter out confounding effects by disregarding negative and insignificant correlations. Additionally, testing our methodology (approach 2) for non-anomalous vegetation 265 productivity (Fig. S8) which allows to compare results with that of Li et al. (2021a), reveals similar results while they use a different methodology based on random forests and Shapley Additive Explanations (SHAP) values which is more robust against confounding effects. Next to this, in our study we apply two different methodologies in approaches 1 and 2 and find similar results, which further underlines the robustness of our conclusions.

Conclusion 270
In this observation-based study, we quantify that vegetation productivity extremes are related to hydrometeorological hazards in about 50% of the global land area that is sufficiently vegetated and cloud-free. The most relevant hazards for vegetation productivity extremes vary along climate gradients. For vegetation productivity maxima the most relevant hydrometeorological extremes are heatwaves in Northern latitudes above 50°N and wet spells in latitudes below 50°N. For productivity minima, drought and cold spells are globally most detrimental to large-scale photosynthesis and carbon uptake. The results of our 275 impact-centric analysis are similar to, and complement more traditional climate-centric studies (Ciais et al., 2005;Flach et al., 2018;Qui et al., 2020). Compound extremes also play a role in 15-20% of our study area, they are somewhat more relevant for productivity minima than for the maxima, with joint drought-heat extremes being most important. Semi-arid, grassdominated ecosystems tend to transition between water and energy control within the range of their productivity variability.
This results in a sensitivity to both water-and energy-related hazards. Thereby, we illustrate how global land-atmosphere 280 coupling hot spots (Koster et al., 2004), where the land surface affects near-surface weather, can be verified using novel vegetation productivity data.
Overall, this study highlights the profound role of (compound) hydrometeorological hazards for global vegetation productivity extremes. Understanding these complex, climate-dependent relationships with present-day observational data is a starting point to more reliably foresee respective changes in a changing future climate with e. g. fewer cold spells but probably more droughts. 285      hydrometeorological hazards co-occurring with the SIF extremes. Box color denotes the main controlling hydrometeorological variable, the second most important variable is indicated in the smaller squares' color, while its size represents the ratio between highest/second highest amounts of grid cells.