# Winter climate preconditioning of summer vegetation extremes in the Northern Hemisphere

Mohit Anand, Raed Hamed, Nora Linscheid, Patrícia S. Silva, Julie Andre, Jakob Zscheischler, Freya K. Garry, Ana Bastos

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Mohit Anand, Raed Hamed, Nora Linscheid, Patrícia S. Silva, Julie Andre, et al. Winter climate preconditioning of summer vegetation extremes in the Northern Hemisphere. Environmental Research Letters, 2024, 19, 10.1088/1748-9326/ad627d. insu-04729835

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# Winter climate preconditioning of summer vegetation extremes in the Northern Hemisphere

To cite this article: Mohit Anand et al 2024 *Environ. Res. Lett.* **19** 094045

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LETTER

Winter climate preconditioning of summer vegetation extremes in
the Northern Hemisphere

Mohit Anand<fnref n="1" />,<fnref n="2" />,* , Raed Hamed<fnref n="3" /> ID, Nora Linscheid<fnref n="4" />, Patrícia S Silva<fnref n="5" /> ID, Julie Andre<fnref n="6" />,<fnref n="7" />,
Jakob Zscheischler<fnref n="1" />,<fnref n="2" />,<fnref n="8" />, Freya K Garry<fnref n="3" /> ID and Ana Bastos<fnref n="4" />,<fnref n="10" /> ID

E-mail: mohit.anand@ufz.de

Keywords: winter climate, leaf area index extremes, legacy effects, vegetation activity, compound events

Abstract

The impact of the spring climate on the Northern Hemisphere's summer vegetation activity and extremes has been extensively researched, but less attention has been devoted to whether and how the winter climate may additionally influence vegetation extremes in the summer. Here, we provide insights into the influence of winter temperature and precipitation on summer vegetation extremes in the Northern Hemisphere. To do this, we identify positive and negative extremes in the summer leaf area index (LAI, a proxy for vegetation activity) and assess winter effects on those extremes using logistic regression at the regional scale. Over a quarter of the regions in the Northern Hemisphere show strong winter climate preconditioning on summer LAI extremes, which is typically stronger for croplands than forests. In regions with strong winter preconditioning, the spring LAI mediates the link between winter climate and summer LAI extremes through the ecological memory in seasonal legacy effects. Our findings suggest that extremely low summer LAI in both croplands and forests is preconditioned by colder and drier winters, while extremely high summer LAI in forests is associated with warmer and wetter winters. For low summer LAI in croplands, warmer winters are associated with an increased likelihood of extremes in mid-latitude regions and a reduced likelihood in high-latitude regions. Consideration of winter preconditioning effects may improve our understanding of inter-annual variability of vegetation activity and support agricultural and land management practitioners in anticipating the detrimental effects of winter on crop yields and forest conditions.

**1. Introduction**

Global vegetation activity is strongly influenced by
the variability in climatic conditions on sub-seasonal
to decadal timescales, with the relative importance
of temperature, radiation and water availability vary-
ing regionally (Ding et al 2020, Menzel et al 2020).
Both the interannual climate variability (Nemani et al

2003, Bastos *et al* 2013, Gonsamo *et al* 2016, Zhu *et al* 2017) as well as climatic trends (Peng *et al* 2011, Piao *et al* 2011, 2014, Barichivich *et al* 2013, Fu *et al* 2015, Peñuelas *et al* 2017, Wang *et al* 2018a) directly influ-
ence the year-to-year differences in vegetation activity
and carbon uptake. Understanding the seasonal pre-
dictability of future vegetation growth that emerges
from climate variability may also assist agricultural

<footnote n="1">¹ Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany</footnote>

<footnote n="2">² Technische Universität Dresden, Dresden, Germany</footnote>

<footnote n="3">³ Department of Water and Climate Risk, Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands</footnote>

<footnote n="4">⁴ Max Planck Institute for Biogeochemistry, Jena, Germany</footnote>

<footnote n="5">⁵ Universidade de Lisboa, Faculdade de Ciências, Instituto Dom Luiz, Lisboa, Portugal</footnote>

<footnote n="6">⁶ Laboratoire de Météorologie Dynamique / Institut Pierre Simon Laplace, ENS—PSL Université, Ecole Polytechnique—Institut Polytechnique de Paris, Sorbonne Université, CNRS, Palaiseau, France</footnote>

<footnote n="7">⁷ Ecole des Ponts, Champs-sur-Marne, France</footnote>

<footnote n="8">⁸ Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden-Leipzig, Germany</footnote>

<footnote n="9">⁹ Met Office, Fitzroy Road, Exeter EX1 3PB, United Kingdom</footnote>

<footnote n="10">¹⁰ Institute for Earth System Science and Remote Sensing, Leipzig University, Leipzig, Germany</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
practitioners with planning and decision-making (e.g. Ogallo et al 2000, Bruno Soares 2017, An-Vo et al 2021).

Memory in land-surface properties—so-called preconditioning or legacy effects—can contribute to the lagged influence of seasonal climate anomalies. For example, winter precipitation and snowmelt can influence vegetation growth over the next growing season in water-limited regions (Peng et al 2010, Chen et al 2018). Vegetation activity itself can also mediate seasonal legacy effects, which can result in contrasting impacts throughout the growing season. Vegetation-mediated legacy effects arise, for example, in response to warmer spring temperatures than usual, resulting in increased springtime plant activity (Buermann et al 2018). This earlier onset of the growing season causes higher water use and faster water depletion until summer (Wolf et al 2016, Lian et al 2020, Bastos et al 2020a, Li et al 2022), with especially severe impacts during warm and dry summers (Bastos et al 2020a, Bevacqua et al 2021). The spring phenology may also influence autumn senescence timing, although the mechanisms of this relationship are not yet fully understood (Liu et al 2016).

While the preconditioning effects of spring warming have been widely studied, the influence of the winter climate on spring and summer vegetation activity is not so well understood. For example, warmer winter temperatures can lead to increased vegetation growth by increasing the water supply from glacial melting (Zhang et al 2016). However, warm winters can also have negative effects on plant growth due to the reduction in protective snow cover (Kreyling 2010) and fewer chilling days (Tominaga et al 2022). Warm winters that induce an earlier onset of the growing season may increase the risk of frost exposure in the leafing, budding and blossoming stages (Marino et al 2011, Bigler and Bugmann 2018), and such events could become more likely under climate change in some regions (Pfleiderer et al 2019, Vautard et al 2023). Short but extreme warming events have been shown to severely damage sub-Arctic landscapes, leading to considerably reduced summer growth (Bokhorst et al 2009).

There is mounting evidence of the influence of winter snow on the timing and productivity of the growing season across the Northern Hemisphere (Wang et al 2018b, Kelsey et al 2021) and the influence of winter precipitation on net primary productivity during the early growing season in forest-grassland ecosystems (Liu et al 2022). Chen et al (2018) investigated the preconditioning effects of winter temperature and precipitation on spring vegetation activity across Europe and North America and also found that reduced winter precipitation limited the spring vegetation activity over water-limited regions. In their study, the inclusion of climate information from the

previous season in regression models improved their capability to predict (the normalized difference vegetation index, a proxy for vegetation greenness). In Central Europe, winter teleconnections (including those associated with the North Atlantic Oscillation, the East Atlantic and the Scandinavian Patterns) significantly influence vegetation productivity during the growing season (Bastos et al 2016, Gonsamo et al 2016, Zhu et al 2017). Winter legacy effects may reach beyond the spring season, and compensatory effects across seasons also have to be considered (Buermann et al 2018, Bastos et al 2020a). To our knowledge, no studies have yet systematically addressed the impact of winter climate on vegetation activity extremes in subsequent summers.

Here, we assess the effect of interannual variability in the winter climate conditions on the vegetation activity extremes in the following summer across the Northern Hemisphere between 1982 and 2020. We use the leaf area index (LAI) as a proxy for vegetation activity and study the effect of winter temperature and precipitation on summer LAI extremes using logistic regression. Our study focuses on sub-continental regions and examines crops and forests separately, given the different controls on phenology, growth and stress responses between these two land cover types (Aspinwall et al 2015, Liu et al 2021, Miguez-Macho and Fan 2021).

## 2. Data and methods

### 2.1. Data

#### 2.1.1. LAI

LAI data for the Northern Hemisphere were obtained from the GLOBMAP global LAI Version 3 product (Liu et al 2012). The GLOBMAP LAI combines data from the Moderate Resolution Imaging Spectroradiometer and the Advanced Very High Resolution Radiometer to provide a long-term LAI dataset from 1981 to 2020 at an 8 km spatial horizontal resolution. The LAI temporal resolution was fortnightly from 1981 to 2000 and at eight-day intervals from 2001 onwards. The LAI data were gridded to a spatial resolution of 0.25° × 0.25° using area-weighted averages. In our analysis, we use data from 1982 to 2020.

#### 2.1.2. Climate variables

Climate data were obtained from the European Centre for Medium-Range Weather Forecasts' ERA5 reanalysis (Hersbach et al 2020). We use monthly total precipitation (P), temperature at 2 m (T), total column volumetric soil water content and snow depth for the period 1982–2020 over the Northern Hemisphere (25°–75° North). ERA5 data are provided on a regular lat/lon grid with 0.25° × 0.25° spatial resolution.
### 2.1.3. Land cover

We use the European Space Agency Land Cover dataset (Santoro et al. 2017) to quantify the fraction of forest and crop cover for each grid cell. The original land cover classification data are provided at 300 m spatial resolution for the period 1982–2019, and we regridded these data to a 0.25° lat/lon grid using the LC-CCI user tool. We use the lookup table from Poulter et al. (2015), reproduced in table A1, to classify crop and forest grid cells. We aggregate the classes 10, 20 and 30 to represent cropland, and the classes 40–100 to represent forest. To minimise the influence of land cover changes in our analysis, only locations that are classified as forest or cropland for the entire 38 years are used for further analysis.

### 2.1.4. Reference regions

To explore the distinct regional dynamics over the Northern Hemisphere, we use the Intergovernmental Panel on Climate Change’s (IPCC) Sixth Assessment Report Working Group I reference regions (Iturbide et al. 2020). Of the 46 land regions, we only consider regions in the Northern Hemisphere and further remove regions if the crop cover or forest cover amounts to less than 100 grid cells. We analyse the vegetation activity of 19 regions in the Northern Hemisphere: 17 regions include both cropland and forest, the Russian Arctic (RAR) region contains only forests, and Eastern Central Asia contains only crops (figure A1). This leads to two different but largely overlapping sets of 18 regions (table A2).

## 2.2. Data preprocessing

We are interested in analysing how the climate affects the interannual variability of the summer LAI. To do this, we preprocess the data by taking the seasonal average precipitation, temperature, soil moisture, snow depth and LAI for boreal winter (December, January and February), spring (March, April and May) and summer (June, July and August). The seasonal averages are linearly detrended to remove potential long-term trends due to climate change since our interest is in the interannual variability. The detrended data are then standardised to unit variance. We identify high (LAIhigh) and low (LAIlow) summer LAI extremes using the local (pixel-wise) 90th and 10th percentiles of the detrended and standardised seasonal LAI averages. To analyse the trends in the fraction of pixels with extreme LAI for each year, we use the Mann–Kendall non-parametric test (Mann 1945, Kendall 1975).

We separately consider croplands and forest areas in our results, referring to low and high summer LAI extremes for croplands as LAI<sub>low</sub><sup>crop</sup> and LAI<sub>high</sub><sup>crop</sup>, and respectively for forest areas as LAI<sub>low</sub><sup>forest</sup> and LAI<sub>high</sub><sup>forest</sup>.

## 2.3. Quantifying winter preconditioning effects

Logistic regression is a suitable method to analyse drivers of extreme impacts (Vogel et al. 2021, Le Grix et al. 2023) and specifically preconditioned compound events (Zscheischler et al. 2020, Bevacqua et al. 2021, Bastos et al. 2023). Here, we extend the approach proposed by Bevacqua et al. (2021) for the study of preconditioning effects of meteorological conditions on LAI extremes.

To quantify the importance of winter preconditioning, we train two separate logistic regression models to predict summer LAI extremes. The first model uses only spring and summer meteorological predictors (temperature and precipitation), while the second model additionally includes winter meteorological predictors. The first model is represented as

$$ \mathcal{P}(\text{LAI}_{\text{extreme}} | \text{w}) = \frac{1}{1 + \exp(-X_{\text{extreme}}^{\text{w}})} \\ \text{with } X_{\text{extreme}}^{\text{w}} = \alpha_0 + \alpha_1 T_{\text{su}} + \alpha_2 P_{\text{su}} \\ + \alpha_3 T_{\text{sp}} + \alpha_4 P_{\text{sp}}. \quad (1) $$

Here, $T_{su}$ and $T_{sp}$ correspond to the temperature in summer and spring, and $P_{su}$ and $P_{sp}$ correspond to the precipitation in summer and spring, respectively. Regression coefficients are given by $\alpha_0, \dots, \alpha_4$. Extreme summer LAI is abbreviated as LAI<sub>extreme</sub>, where extremes can be low or high. The term $\text{w}$ indicates that winter climate was not included for this particular model. The model is used to predict how likely the occurrence of extreme summer LAI in summer is without considering the influence of winter climate, $\mathcal{P}(\text{LAI}_{\text{extreme}} | \text{w})$.

The second model additionally includes the winter temperature ($T_w$) and winter precipitation ($P_w$) as well as their corresponding regression coefficients $\alpha_5$ and $\alpha_6$:

$$ \mathcal{P}(\text{LAI}_{\text{extreme}} | \text{w}) = \frac{1}{1 + \exp(-X_{\text{extreme}}^{\text{w}})} \\ \text{with } X_{\text{extreme}}^{\text{w}} = \alpha_0 + \alpha_1 T_{\text{su}} + \alpha_2 P_{\text{su}} + \alpha_3 T_{\text{sp}} \\ + \alpha_4 P_{\text{sp}} + \alpha_5 T_w + \alpha_6 P_w. \quad (2) $$

We train the logistic regression models based on pooled data for cropland and forest pixels separately and for each region. This increases the sample size, allowing us to analyse regional patterns but not sub-regional variability. We further consider LAI<sub>high</sub> and LAI<sub>low</sub> separately so that we consider 72 pairs of models for the Northern Hemisphere (two land-cover types, 18 regions, two types of extremes). The sample size $N$ varies per region depending on the number of crop and forest pixels within that region ($N = n_{\text{years}} \times n_{\text{pixels}}$, table A2).

Rearranging the logistic regression equation, $X_{\text{extreme}} = \ln(\frac{\mathcal{P}(\text{LAI}_{\text{extreme}})}{1 - \mathcal{P}(\text{LAI}_{\text{extreme}})})$, which are the log odds.
Hence, for interpretation of the fitted model, we
take the exponential raised to the power of the
coefficient of the logistic regression model as they
provide a more direct interpretation with respect
to the odds of experiencing an extreme over no
extreme, $\frac{IP(\text{LAI}_{\text{extreme}})}{1-IP(\text{LAI}_{\text{extreme}})}$; for example, for a unit increase
in $T_w$ or $P_w$ and keeping all the other variables
fixed, the odds of experiencing an extreme relat-
ive to a non-extreme LAI change by $e^{\alpha_5}$ or $e^{\alpha_6}$,
respectively.

The model without winter climate (equation (1))
is a nested model with respect to the model with
winter climate (equation (2)), as the latter contains all
the parameters of the former as well as the paramet-
ers α5 and α6. We therefore test whether including the
winter climate significantly improves the prediction
of summer LAI extremes using the likelihood ratio
test with significance level α = 0.05. The likelihood
ratio test assesses the goodness of fit of two competing
nested statistical models and provides a p-value asso-
ciated with the null hypothesis that both models are
equally good. A low p-value therefore means that the
model with more parameters is better than the model
with fewer parameters.

Given the very large sample size for each region
(table A2), the likelihood ratio test might show sig-
nificant differences even when the differences in pre-
dictability between the models are very small. To rank
model differences across regions, we use the Receiver
Operating Characteristics curve, which is the plot of
the true positive rate against the false positive rate for
different threshold settings of a binary classifier. The
area under the receiver operating characteristics curve
(AUROC) is then a predictive performance metric
ranging between 0 and 1, with a value of 0.5 represent-
ing prediction by random chance and a value of 1 rep-
resenting perfect prediction. We quantify the strength
of winter preconditioning based on the difference
in AUROC between models with winter predictors
(AUROC|w) and models without winter predictors
(AUROC|\ w).

For regions with strong winter preconditioning,
we further analyse the standardised seasonal averages
of anomalies in meteorological variables (i.e. com-
posites) in the years with extreme LAI in summer to
complement the interpretation of the results based on
the logistic regression coefficients. We create compos-
ites from the predictors used in the logistic regression
and also consider a few additional variables: winter,
spring and summer precipitation, temperature, soil
moisture, winter and spring snow depth, and spring
LAI. As there is no snow in summer in many of the
regions, we exclude summer snow from our analysis.
We also exclude winter LAI from our analysis because
of the very low vegetation activity in winter in the
Northern Hemisphere.

3. Results

**3.1. Occurrence of summer LAI extremes**

There is large interannual variability in the spatial extent of LAI extremes between 1982 and 2020 for each of the IPCC regions in the Northern Hemisphere (figure 1). Several regions show high temporal correlation between the cropland and forest extents experiencing LAI extremes (numbers in figure 1). The correlation of LAI<sub>low</sub> between crops and forests tends to reach higher values (seven regions showing correlations above 0.6) than for LAI<sub>high</sub> (four regions). The Mediterranean (MED) region shows the highest temporal correlation of all regions for both LAI<sub>low</sub> and LAI<sub>high</sub>.

Five regions show a significantly increasing trend
in the extent of LAI<sub>low</sub><sup>crop</sup>, and ten regions show sig-
nificantly increasing extents of LAI<sub>high</sub><sup>crop</sup> (*p* < 0.05,
table A3). The only region showing a significantly
increasing trend (*p* < 0.05) in the extent of LAI<sub>low</sub><sup>forest</sup>
is the RAR.

**3.2. Preconditioning effect of winter climate on LAI extremes**

In general, we find that extremes over cropland
are better predicted compared to forests (tables A4
and A5). In all regions, the winter climate signific-
antly improves the prediction of extreme LAI in sum-
mers, as determined by the likelihood ratio test of the
logistic regression models in equations (1) and (2)
(*p* < 0.05). However, despite the effect of the winter
climate being significant, in many regions the pre-
conditioning strength is small (overall, AUROC|w
minus AUROC|\ w ranges between 0 and 0.08;
tables A4 and A5). For the subsequent analysis, we
consider regions with preconditioning strength lar-
ger than 0.02 as winter ‘preconditioned regions’. This
threshold is fixed for all regions and the subjective
choice is based on visual investigation of the strength
of winter preconditioning for both LAI<sub>low</sub> and LAI<sub>high</sub>
for crops and forests (figures 2(a), (c), (e) and (g)).
This results in five preconditioned regions for both
LAI<sub>low</sub><sup>crop</sup> and LAI<sub>high</sub><sup>crop</sup>, eight preconditioned regions for
LAI<sub>low</sub><sup>forests</sup>, and five for LAI<sub>high</sub><sup>forests</sup> (figure 2). East Asia
(EAS) is the only region where winter precondition-
ing effects are found for both LAI<sub>low</sub><sup>crop</sup> and LAI<sub>high</sub><sup>crop</sup>.
Eastern Siberia (ESB) and RAR are the only regions
with high winter preconditioning for both LAI<sub>low</sub><sup>forests</sup>
and LAI<sub>high</sub><sup>forests</sup>.

For LAI<sub>low</sub>, the change in odds ratios associ-
ated with P<sub>w</sub> (e<sup>α6</sup>) is predominantly below 1 across
most preconditioned regions for both cropland and
forest (figure 3(a), y-axis). This indicates that posit-
ive P<sub>w</sub> anomalies decrease the odds of LAI<sub>low</sub> com-
pared to non-extreme summer LAI in these regions.
For instance, the decrease in odds of LAI<sub>low</sub> is more
than 40% for Western Central Asia (WCA) for a one
<figure><img src="image_4.png" /></figure>

standard deviation increase in $P_w$ over croplands. A notable exception is EAS, where no effect (forests) or the opposite effect (crops) of $P_w$ is observed. In contrast, the effects of $T_w$ ($e^{\alpha_5}$) are more variable across regions (figure 3(a), x-axis) with the odds of LAI$_{low}$ changing between -35% and +40% for a one standard deviation increase in $T_w$. Four regions show positive effects of $T_w$ on LAI$_{low}$, either in croplands (MED), forests (Central North America (CNA)), or both croplands and forests (EAS, WCA). Two regions show negative $T_w$ effects (Northeastern North America (NEN), Western Siberia (WSB)) for both croplands

and forests, and two regions show negative effects of $T_w$ in forests (Russian Far East (RFE), ESB). These differences largely correspond to background climate conditions: warmer regions (below 40° N) consistently show that positive $T_w$ anomalies increase the odds of LAI$_{low}$. Conversely, cooler regions (above 40° N) consistently show that positive $T_w$ anomalies decrease the odds of LAI$_{low}$.

For LAI$_{high}$, we observe a symmetric effect of $T_w$, with positive $T_w$ anomalies increasing the odds of LAI$_{high}$ in the northern regions but decreasing them in southern regions (figure 3(b), x-axis). Positive $P_w$
<figure><img src="image_5.png" /></figure>

anomalies increase the odds of LAI<sub>high</sub> in northern regions and decrease them in southern regions, with the exception of crops in North Central America (NCA) that show positive *P*<sub>w</sub> anomalies increase the odds of LAI<sub>high</sub> (figure 3(b), y-axis). The effect of *T*<sub>w</sub> on the odds ratios of LAI<sub>high</sub> is stronger than that of *P*<sub>w</sub>, with changes of -43% to 58% for one standard deviation change in *T*<sub>w</sub>, compared to -25% to 38% for *P*<sub>w</sub>.

To understand how relevant the effect of winter
is, compared to spring and summer, we compute
the odds ratios associated with all predictors for all
seasons (tables A6–A9). In many winter precondi-
tioned regions, the effect of a change in T<sub>w</sub> is stronger
than the effect of a change in the spring or summer
climate. This holds for two cropland regions (EAS,
NEN) and three forest regions (CNA, WSB, ESB) for
LAI<sub>low</sub>. Similarly, in three crop regions (Northwestern
North America (NWN), South Asia (SAS), Tibetan

Plateau (TIB)) and one forest region (ESB), changing
T<sub>w</sub> leads to the largest change in the odds of LAI<sub>high</sub>.
P<sub>w</sub> is affecting the odds of LAI<sub>low</sub> the most in two
regions (MED for croplands and WCA for forests).
Even though in some regions neither T<sub>w</sub> nor P<sub>w</sub> lead
to the strongest odds ratio, the effects of winter cli-
mate are often stronger than many predictors from
spring or summer. For LAI<sub>low</sub>, P<sub>w</sub> is particularly rel-
evant in forests, with seven out of eight regions show-
ing a more strongly reduced odds ratio for P<sub>w</sub> than
those associated with precipitation in the spring and
summer (CNA, ESB, NEN, RAR, RFE, WCA, WSB).
Such a strong effect of P<sub>w</sub> compared to spring or sum-
mer is only found in two out of the five LAI<sub>low</sub><sup>crop</sup> regions
(MED, NEN). For LAI<sub>high</sub> regions, the effect of P<sub>w</sub> can
be stronger than that of spring (TIB for croplands and
Eastern Europe (EEU) for forests), summer (ESB and
Northern Europe (NEU) for forests), or spring and
summer (EAS for croplands, Eastern North America
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Importance of winter temperature ($T_w$) and precipitation ($P_w$) for predicting (a) extremely low (LAI<sub>low</sub>) and (b) extremely high (LAI<sub>high</sub>) summer LAI. Displayed are the odds ratios of experiencing an LAI extreme relative to no extreme associated with a one standard deviation increase in $T_w$ (x-axis) and $P_w$ (y-axis), assuming all other variables being fixed, for crops (orange) and forests (green). The numbers are based on the coefficients of the logistic regression model ($\alpha_5$ and $\alpha_6$, respectively, from equation (2)). The preconditioned regions are coloured, all other regions are in grey.</figcaption></figure>

(ENA) and RAR for forests) precipitation. Even when $T_w$ is not the most relevant predictor for LAI<sub>high</sub> (see above), in many regions the effect is still stronger than that of spring (in NCA and EAS croplands and EEU and NEU forests), and stronger than or comparable to that of summer (in ENA forests and EAS croplands, respectively).

### 3.3. Direct and indirect climate contributions to summer extremes in winter preconditioned regions

Our analysis reveals that in many regions, winter climate conditions have substantial preconditioning effects on summer LAI extremes. The contribution of a given climatic driver in each season to summer LAI extremes depends not only on the sensitivity of vegetation to the driver but also on the magnitude of the anomalies experienced in those years. Furthermore, for those regions where winter preconditioning effects are relevant, the effects might be direct (e.g. with winter frost leading to vegetation damage) or indirect (e.g. through the winter climate's influence on spring LAI, which then impacts the summer LAI). To further understand the link between winter preconditioning and summer LAI extremes, we quantify the composites of anomalies in *T*, *P* and soil moisture in winter, spring and summer; snow depth in winter and spring; and LAI in spring.

Winters corresponding to LAI<sub>low</sub> in croplands and forests (figures 4(a) and (b)) tend to be drier than average, with predominantly negative anomalies in $P_w$, $SM_w$ and $SD_w$. Especially for forests, they also tend to be colder than average (negative $T_w$

anomalies). The strongest negative $T_w$ anomalies are experienced by WSB (−0.44σ for forests and −0.4σ for croplands), while croplands in WCA, MED and WSB and forests in WCA and NEN show the strongest negative $P_w$ anomalies (below −0.4σ). Exceptions to this pattern are EAS for croplands and forests and CNA for forests, where LAI<sub>low</sub> is associated with warmer than average winters, and WCA and MED, where $T_w$ anomalies are close to neutral.

In the spring and summer of LAI<sub>low</sub> years, both croplands and forests show persisting negative anomalies in water-related variables in the spring (precipitation, soil moisture and snow depth), but show opposite anomalies in the summer: croplands are associated with drier and forests with wetter than average conditions for LAI<sub>low</sub> years. Temperature anomalies are strongly positive in the spring and summer for croplands (except NEN), especially WCA, MED and WSB (with magnitudes >0.5σ). However, for forests, a switch from normal or warmer-than-average spring conditions to cold summers is found. CNA and WCA are exceptions to this pattern over forests, showing a pattern more consistent with croplands of persisting warmer and drier conditions over spring and summer. In some regions, spring is already associated with lower than average LAI, particularly WCA and MED over croplands and EAS for both croplands and forests. This indicates a potentially indirect effect of winter preconditioning through spring LAI for these regions. However, most regions do not show strongly negative spring LAI anomalies, and RAR even shows positive spring LAI (0.23σ), indicating that winter preconditioning effects on the
<figure><img src="image_7.png" /></figure>

negative summer LAI extremes are unlikely to be only
due to their effects on spring vegetation activity.

Preconditioned regions with LAI<sub>high</sub> extremes tend to show opposite patterns for precipitation anomalies in years with LAI<sub>low</sub> extremes for croplands and forests: most regions experience wetter than average winter and spring conditions, while summer precipitation is above average for croplands and below average for forests. The few exceptions, EAS and NCA for croplands and RAR and ENA for forests, show below-average anomalies in one water-related variable (e.g. SD<sub>w</sub> in NCA croplands), but these anomalies are compensated by positive anomalies in the other water-related variables. T<sub>w</sub> anomalies are predominantly strong and negative (−0.50σ to −0.22σ) across most preconditioned cropland regions. Unlike croplands, T<sub>w</sub> over forest regions is predominantly positive for LAI<sub>high</sub> extremes (0.21σ–0.25σ) except ENA (−0.17σ).

The increased water availability during years with LAI<sub>high</sub> extremes through winter and spring leads to a strong positive LAI anomaly already in the spring for all cropland (0.18σ–0.60σ) and, less markedly, for most forest regions (0.06σ–0.33σ) except NEU (–0.15σ). In the summer, as for LAI<sub>low</sub>, croplands and forest regions show opposing climate anomaly patterns: cropland regions are associated with colder and wetter summers with the exception of TIB, with a small but negative summer precipitation anomaly (–0.08σ); in forest regions LAI<sub>high</sub> extremes are

associated with drier and warmer conditions in all
regions, although the anomalies are small for ENA.

4. Discussion

4.1. Patterns and trends in LAI extremes

Extremes for LAI<sub>low</sub> tend to co-occur in croplands and forests more than LAI<sub>high</sub> extremes, possibly because the drivers of LAI<sub>low</sub> extremes (mostly water limitation) are more consistent between croplands and forests than those of LAI<sub>high</sub> extremes (figures 3 and 4). However, this could also be due to the relatively coarse resolution of the LAI data (0.25° × 0.25°), which does not allow for full separation of croplands and forests where the land cover heterogeneity is high.

Several regions in the Northern Hemisphere show significant positive trends in the spatial extent of extreme summer LAI over croplands (five regions for LAI<sub>low</sub><sup>crop</sup> and ten for LAI<sub>high</sub><sup>crop</sup>). The positive trends in LAI<sub>low</sub><sup>crop</sup> could be associated with an increase in the spatial extent of compound hot-dry events, e.g. in China (Yang et al 2023), India (Chen et al 2016, Sharma and Mujumdar 2017, Mishra et al 2020), Europe (Manning et al 2019) and in central and western North America (Alizadeh et al 2020). The positive trends in the spatial extent of LAI<sub>high</sub><sup>crop</sup> might be explained by the increased availability of nutrients (through fertilisers and deposition) that contribute to increased crop productivity and growth, especially
during favourable years (Zeng *et al* 2014). For forest regions, the spatial extent of LAI extremes is roughly stable over time (with only one region with a significant trend for LAI<sub>low</sub><sup>forest</sup>). This might be due to the higher resistance of forests to heat and drought stress (Flach *et al* 2018, Xiao *et al* 2023), their longer development times (multi-annual) compared to many croplands (seasonal) as well as the higher diversity in traits influencing their stress responses (Martínez-Vilalta *et al* 2014, Aspinwall *et al* 2015, Anderegg *et al* 2016).

## 4.2. Winter climate is preconditioning LAI extremes

We show that the winter climate preconditions LAI<sub>low</sub> and LAI<sub>high</sub> extremes across large parts of the Northern Hemisphere. In many of the winter preconditioned regions, both high and low extreme summer LAI events are more strongly influenced by winter precipitation and temperature than by spring and summer conditions (tables A6–A9). Odds ratios range from 0.54 to 1.58, indicating an up to 58% change in the odds of occurrence of LAI extremes due to one standard deviation increase in the winter temperature or precipitation. This underscores the critical role of the winter climate in determining extreme summer LAI events, independent of spring and summer weather conditions. Some regions emerge as being particularly sensitive to winter climate conditions: EAS for LAI<sub>high</sub><sup>crop</sup>, LAI<sub>low</sub><sup>crop</sup> and LAI<sub>low</sub><sup>forest</sup>, WCA, WSB and NEN for LAI<sub>high</sub><sup>crop</sup> and LAI<sub>low</sub><sup>crop</sup>, and RAR for LAI<sub>high</sub><sup>forest</sup> and LAI<sub>low</sub><sup>forest</sup>, showing strong winter preconditioning effects.

Similarities between forests and crops are found for low LAI extremes: drier winter conditions generally increase the odds of LAI<sub>low</sub> (figure 3), and consistently, LAI<sub>low</sub> extremes are generally preceded by dry winters (figure 4). Among the preconditioned regions, MED and WCA show the strongest sensitivity to winter precipitation and also the strongest negative anomalies in winter precipitation for extreme years. In these regions, summers are typically dry and the growing season starts earlier than in other regions, so drought extremes are more relevant when occurring in the winter and early spring (El-Madany *et al* 2020). Long droughts that span multiple months, including months before planting, have been shown to lead to lower-than-normal yields in various parts of the globe (Zampieri *et al* 2017, Webber *et al* 2018, Santini *et al* 2022). Droughts also have an adverse effect on forest productivity (Park Williams *et al* 2013, Anderegg *et al* 2015, Pohl *et al* 2023, van der Woude *et al* 2023).

Consistent with negative winter precipitation anomalies preceding LAI<sub>low</sub> extremes, we observe negative snow cover anomalies in the winter, which are still prevalent in the spring in all of the preconditioned regions. Reduced snow cover in winter can lead

to a reduction of soil temperature due to decreased bulk thermal insulation (Henry *et al* 2018, Zhang *et al* 2018). Such conditions have been shown to significantly reduce winter crop productivity by increasing the sensitivity to freezing stress (Trnka *et al* 2014, Zhu *et al* 2019, 2022, Beillouin *et al* 2020). Additionally, the reduced snowpack coupled with below average precipitation in winter results in low soil water availability during the spring (figure 4), inhibiting the development of vegetation at the onset of the growing season (Buermann *et al* 2018, Heino *et al* 2023).

In contrast to the precipitation effects, the temperature effects are more complex, with large variability in the odds of LAI<sub>low</sub> for both croplands and forests (figure 3), and opposite patterns for crops and forests for LAI<sub>high</sub>; in forests, warmer winters tend to increase the likelihood of LAI<sub>high</sub>, and vice versa for croplands. Consistently, the years of LAI<sub>high</sub><sup>crop</sup> are associated with colder than average temperatures, while LAI<sub>high</sub><sup>forest</sup> years show warmer than average temperatures. This is supported by Zscheischler *et al* (2014), who found that low productivity extremes are associated with colder conditions in boreal forests in multiple vegetation models. Overall, higher winter temperatures reduce the risk of frost days, which impedes tree growth and regeneration (Girardin *et al* 2022).

In croplands, the sensitivity of LAI<sub>low</sub> to winter temperature follows a latitudinal gradient: warmer winters are associated with a higher likelihood of LAI<sub>low</sub> in mid-latitude regions and a reduced likelihood of LAI<sub>low</sub> in high-latitude regions. This is supported by a regional study in Sweden, where increased temperatures favoured crop yields in the north but had a negative impact in the south over the period 1965–2020 (Sjulgård *et al* 2023). The relevance of warm winter temperatures for extremely low crop yields has also been highlighted for Europe in previous studies (Ben-Ari *et al* 2018, Beillouin *et al* 2020).

## 4.3. Winter preconditioning is modulated by spring LAI

In most preconditioned regions, the winter climate effect is already visible in the spring LAI, with similar directions in spring anomalies to those of summer (figure 4). This is consistent with the importance of memory effects in ecological processes (Ogle *et al* 2015, Cranko Page *et al* 2021) and seasonal legacy effects between spring and summer (Buermann *et al* 2018), including for extremes (Wolf *et al* 2016, Bastos *et al* 2020a, Bevacqua *et al* 2021). The climate and spring LAI anomalies in extreme years are generally stronger for crops than forests, reflecting the former's faster response times and also leading to better predictability of extreme years over croplands (tables A4 and A5). The higher predictability over cropland areas may thus be explained by the limited legacy effects of multi-year climate variations
on crops, which are typically annual plants, whereas forest growth and functioning are more likely to show multi-annual legacy effects (Bastos *et al* 2021, Yu *et al* 2022, Cranko Page *et al* 2023a, Anand *et al* 2024). For instance, droughts can have an effect on forest vegetation activity even two years after their occurrence (Yu *et al* 2022), which by far exceeds the nine-month time-span studied here, but could contribute to some variability in the results.

Interestingly, in three snow-dominated regions over forests, we see a reverse direction in the spring LAI anomalies compared to summer LAI anomalies. Specifically, for LAI<sub>low</sub><sup>forests</sup> in RAR and RFE, higher than usual spring LAI (>0.1) anomalies are observed, which might appear counter-intuitive. However, both regions are also associated with low spring snow cover anomalies, aiding in early greening in these years, but are followed by strongly negative summer temperature anomalies (< -0.4) and slightly wetter than average summer conditions. The cold anomalies in the summers, especially for those regions with short growing seasons, might result in damage to the newly budded leaves, leading to LAI<sub>low</sub><sup>forests</sup>.

Over NEU for LAI<sub>high</sub><sup>forests</sup>, we see a negative anomaly for spring LAI (-0.15σ), but these are followed by warmer and slightly drier summers. As the corresponding spring temperatures are not warmer, vegetation development may only catch up in summers but is favoured by the previous seasons' average moisture. Wang *et al* (2018b) also analysed the impact of snow water equivalent (SWE) in the Northern Hemisphere and found that higher SWE leads to a negative growth effect on vegetation by delaying the snowmelt and reducing the vegetative period, explaining the negative anomalies in the spring LAI. Furthermore, many of these high-latitude regions are typically energy-limited in summer, so colder than average and wetter (i.e. more cloudy) summers tend to be associated with low forest productivity and growth, and the opposite for warm and dry summers (Walther *et al* 2019, Bastos *et al* 2020b).

### 4.4. Compounding drivers and possible extensions

Large anomalies occur in different variables and seasons, highlighting the compounding nature of the drivers of extreme summer LAI events. For instance, a drier winter followed by a hotter and drier spring and summer typically leads to LAI<sub>low</sub><sup>crop</sup> in WCA, MED and WSB. To fully understand the risks associated with such compound events, multivariate approaches are required that take into account the correlation structure across time and between variables (Ogle *et al* 2021, Cranko Page *et al* 2023a). For instance, Zscheischler *et al* (2017) found that bivariate return periods of precipitation and temperature can well explain crop yields in different European countries. Similarly, using a new index tailored to

bivariate variations in temperature and precipitation, Li *et al* (2022) found that compound warm-dry events increase vegetation productivity at high latitudes, consistent with the fact that these ecosystems are typically energy-limited in the summer. In contrast, such events reduce productivity in the mid-latitudes, and compound warm-dry springs can cause and amplify summer droughts, thereby reducing summer productivity.

Our study provides a first report on the large-scale winter preconditioning effects of summer LAI extremes. However, we note that we do not explore the effects of ecological and human factors in modulating the impacts on LAI (Bastos *et al* 2023). Such modulating factors include regional differences in phenology, the effects of landscape management, irrigation, topography, succession, as well as climate-driven disturbances such as fire or insects (Seidl *et al* 2017). However, we expect that these processes play minor roles at the coarse spatial and temporal resolution considered here. As such, our study lays the basis for more detailed, high-resolution regional analyses. Our approach is anchored in identifying linear relationships with no interactions, which eases the interpretation but with limited predictive skill. Nonlinear approaches based on machine learning can be combined with interpretation techniques to identify drivers of impact extremes (Jiang *et al* 2022, 2024) but require careful model calibration when working with spatiotemporal data (Sweet *et al* 2023). We also do not identify causal relationships between variables. As our results indicate multiple relevant climate anomalies across seasons, chains of weather patterns and their impacts (like droughts and heat waves) should be further studied to identify causal links and carry-over effects across seasons. Such causal links could be investigated using process-based models that simulate the climatic and non-climatic controls for vegetation functioning, structure and dynamics.

## 5. Conclusions

In this study, we analyse the influence of winter climate on summer LAI extremes for croplands and forests in the Northern Hemisphere. We define winter preconditioning strength based on the ability of the winter climate to improve the prediction of extremes in the summer LAI in comparison to using only spring and summer climate information. Winter preconditions the occurrence of summer LAI extremes for both crops and forests in more than a quarter of the IPCC Northern Hemisphere regions studied. We find that winter climate anomalies allow a better explanation of summer LAI extremes in many regions, but with importance differences between crops and forests and across regions. Low LAI extremes in summer are typically associated with
colder and drier winter conditions in most regions. More diverse responses are identified for high LAI extremes in summer, with the differences being particularly large between crops and forests. Generally, in forests, high summer LAI extremes are associated with warmer and wetter winters. For crops, we also find that the effect of winter climate on high LAI extremes in summer is dependent on latitude. In many regions, the influence of winter climate is already visible in the spring LAI, highlighting the importance of ecological memory in seasonal legacy effects. Moreover, the observed anomalies in different climate and state variables across different seasons illustrate the compound nature of the drivers of extreme LAI events. In particular, the winter temperature and precipitation precondition the summer LAI through multiple variables (soil moisture, snow depth and LAI) over winter and spring, highlighting the challenges associated with identifying drivers of summer LAI extremes and the importance of ecological memory for impact assessments. Overall, our study presents evidence for lagged climate and ecological effects spanning from winter to summer for many regions in the Northern Hemisphere, and provides a basis for more in-depth studies at the regional scale.

## Data availability statement

All datasets that support the findings of this study are openly available. The GLOBMAP global leaf area index data is available on Zenodo at https://doi.org/10.5281/zenodo.4700264. ERA5 reanalysis datasets are freely available for download from the Copernicus website (https://cds.climate.copernicus.eu/). Landcover data can be downloaded from the ESA LandcoverCCI website (https://www.esa-landcover-cci.org/).

No new data were created or analysed in this study.

## Acknowledgments

The authors thank the DAMOCLES COST Action CA17109, which funded a Compound Events Training School where this work commenced. M A and J Z acknowledge funding from the Helmholtz Initiative and Networking Fund (Young Investigator Group COMPOUNDX, Grant Agreement VH-NG-1537). R H acknowledges funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement 820712 (RECEIPT). N L, A B, R H and J Z acknowledge funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 101003469 (XAIDA). P S S was supported by Fundação para a Ciência e a Tecnologia (FCT), Grant Number SFRH/BD/146646/2019. F K G was supported by the Strategic Priority Fund for UK Climate Resilience. The UK Climate Resilience programme was supported by the UK Research and Innovation Strategic Priorities Fund, and was co-delivered by the Met Office and NERC on behalf of UK Research and Innovation partners AHRC, EPSRC and ESRC. The contribution of F K G is subject to © Crown Copyright, Met Office. A B acknowledges funding by the European Union (ERC StG, ForExD, Grant Agreement No. 101039567).

## Author contributions

M A, R H, N L, J Z and A B conceptualised the article and defined the methodology. M A wrote the original draft and did the formal analysis. P S S, J A and F K G contributed to the development of the article through discussion in the initial stage. All the authors contributed to the revision of the manuscript. R H and N L contributed equally.
## Appendix. Additional figures and tables

<figure><img src="image_8.png" /><figcaption>**Table A1.** Crop vs forest definition in this study.</figcaption></figure>

<table><caption>**Table A2.** Total number of pixels and forest and crop fraction in the IPCC regions.</caption><thead><tr><th>Class</th><th>Description</th><th>Type</th></tr></thead><tbody><tr><td>10</td><td>Cropland, rainfed</td><td>Crop</td></tr><tr><td>20</td><td>Cropland, irrigated or post-flooding</td><td>Crop</td></tr><tr><td>30</td><td>Mosaic cropland (&gt;50 %) nat. veg. (tree, shrub, herb.) (&lt;50 %)</td><td>Crop</td></tr><tr><td>40</td><td>Mosaic nat. veg. (tree, shrub, herb.) (&gt;50 %)/cropland (&lt;50 %)</td><td>Forest</td></tr><tr><td>50</td><td>Tree cover, broadleaf, evergreen, closed to open (&gt;15 %)</td><td>Forest</td></tr><tr><td>60</td><td>Tree cover, broadleaf, deciduous, closed to open (&gt;15 %)</td><td>Forest</td></tr><tr><td>70</td><td>Tree cover, needleleaf, evergreen, closed to open (&gt;15 %)</td><td>Forest</td></tr><tr><td>80</td><td>Tree cover, needleleaf, deciduous, closed to open (&gt;15 %)</td><td>Forest</td></tr><tr><td>90</td><td>Tree cover, mixed leaf type (broadleaf and needleleaf)</td><td>Forest</td></tr><tr><td>100</td><td>Mosaic tree and shrub (&gt;50 %)/herbaceous cover (&lt;50 %)</td><td>Forest</td></tr></tbody></table>

<table><thead><tr><th>IPCC region (abbreviation)</th><th>Crop fraction (%)</th><th>Forest fraction (%)</th><th>Total pixels</th></tr></thead><tbody><tr><td>Central North America (CNA)</td><td>33.80</td><td>16.95</td><td>4944</td></tr><tr><td>East Asia (EAS)</td><td>20.85</td><td>22.98</td><td>118 46</td></tr><tr><td>Eastern Central Asia (ECA)</td><td>3.92</td><td>0</td><td>4800</td></tr><tr><td>Eastern Europe (EEU)</td><td>27.92</td><td>44.67</td><td>6400</td></tr><tr><td>Eastern North America (ENA)</td><td>9.09</td><td>37.27</td><td>9638</td></tr><tr><td>Eastern Siberia (ESB)</td><td>4.60</td><td>72.09</td><td>128 00</td></tr><tr><td>Mediterranean (MED)</td><td>16.65</td><td>12.35</td><td>120 00</td></tr><tr><td>North Central America (NCA)</td><td>3.55</td><td>11.54</td><td>2876</td></tr><tr><td>Northeastern North America (NEN)</td><td>1.25</td><td>19.85</td><td>192 59</td></tr><tr><td>Northern Europe (NEU)</td><td>2.58</td><td>23.41</td><td>142 80</td></tr><tr><td>Northwestern North America (NWN)</td><td>2.98</td><td>32.54</td><td>206 72</td></tr><tr><td>Russian Arctic (RAR)</td><td>0</td><td>26.68</td><td>235 89</td></tr><tr><td>Russian Far East (RFE)</td><td>1.13</td><td>40.06</td><td>121 38</td></tr><tr><td>South Asia (SAS)</td><td>42.58</td><td>19.12</td><td>2400</td></tr><tr><td>Tibetan Plateau (TIB)</td><td>4.58</td><td>11.08</td><td>3600</td></tr><tr><td>Western Central Asia (WCA)</td><td>16.28</td><td>5.01</td><td>9010</td></tr><tr><td>Western North America (WNA)</td><td>6.63</td><td>30.91</td><td>5522</td></tr><tr><td>Western Siberia (WSB)</td><td>16.94</td><td>31.13</td><td>9600</td></tr><tr><td>West & Central Europe (WCE)</td><td>52.29</td><td>26.26</td><td>7720</td></tr></tbody></table>
<table><caption>**Table A5.** AUROC curves for the prediction of LAI<sub>high</sub>. Scores are bold for preconditioned regions.</caption><thead><tr><th>IPCC region (abbreviation)</th><th>Crop<sup>\w</sup></th><th>Crop<sup>\w</sup></th><th>Forest<sup>\w</sup></th><th>Forest<sup>\w</sup></th></tr></thead><tbody><tr><td>Central North America (CNA)</td><td>0.623</td><td>0.625</td><td>0.583</td><td>0.592</td></tr><tr><td>East Asia (EAS)</td><td><strong>0.587</strong></td><td><strong>0.651</strong></td><td>0.558</td><td>0.567</td></tr><tr><td>Eastern Central Asia (ECA)</td><td>0.712</td><td>0.721</td><td>—</td><td>—</td></tr><tr><td>Eastern Europe (EEU)</td><td>0.754</td><td>0.756</td><td><strong>0.615</strong></td><td><strong>0.638</strong></td></tr><tr><td>Eastern North America (ENA)</td><td>0.648</td><td>0.666</td><td><strong>0.561</strong></td><td><strong>0.585</strong></td></tr><tr><td>Eastern Siberia (ESB)</td><td>0.664</td><td>0.677</td><td><strong>0.591</strong></td><td><strong>0.624</strong></td></tr><tr><td>Mediterranean (MED)</td><td>0.795</td><td>0.799</td><td>0.733</td><td>0.735</td></tr><tr><td>North Central America (NCA)</td><td><strong>0.824</strong></td><td><strong>0.849</strong></td><td>0.730</td><td>0.733</td></tr><tr><td>Northeastern North America (NEN)</td><td>0.595</td><td>0.600</td><td>0.681</td><td>0.683</td></tr><tr><td>Northern Europe (NEU)</td><td>0.680</td><td>0.697</td><td><strong>0.669</strong></td><td><strong>0.701</strong></td></tr><tr><td>Northwestern North America (NWN)</td><td><strong>0.637</strong></td><td><strong>0.669</strong></td><td>0.645</td><td>0.661</td></tr><tr><td>Russian Arctic (RAR)</td><td>—</td><td>—</td><td><strong>0.697</strong></td><td><strong>0.723</strong></td></tr><tr><td>Russian Far East (RFE)</td><td>0.566</td><td>0.558</td><td>0.627</td><td>0.638</td></tr><tr><td>South Asia (SAS)</td><td><strong>0.639</strong></td><td><strong>0.660</strong></td><td>0.586</td><td>0.595</td></tr><tr><td>Tibetan Plateau (TIB)</td><td><strong>0.596</strong></td><td><strong>0.677</strong></td><td>0.639</td><td>0.655</td></tr><tr><td>Western Central Asia (WCA)</td><td>0.735</td><td>0.754</td><td>0.653</td><td>0.663</td></tr><tr><td>Western North America (WNA)</td><td>0.700</td><td>0.702</td><td>0.633</td><td>0.640</td></tr><tr><td>Western Siberia (WSB)</td><td>0.717</td><td>0.734</td><td>0.626</td><td>0.641</td></tr><tr><td>West & Central Europe (WCE)</td><td>0.697</td><td>0.698</td><td>0.585</td><td>0.591</td></tr></tbody></table>

<table><caption>**Table A6.** Odds ratio of different regions for LAI<sub>low</sub>. Winter preconditioned regions are in bold.</caption><thead><tr><th>IPCC region (abbreviation)</th><th>T<sub>w</sub></th><th>P<sub>w</sub></th><th>T<sub>sp</sub></th><th>P<sub>sp</sub></th><th>T<sub>su</sub></th><th>P<sub>su</sub></th></tr></thead><tbody><tr><td>Central North America (CNA)</td><td>1.044</td><td>1.119</td><td>0.894</td><td>0.966</td><td>1.499</td><td>0.71</td></tr><tr><td><strong>East Asia (EAS)</strong></td><td>1.442</td><td>1.11</td><td>0.987</td><td>0.828</td><td>1.392</td><td>1.122</td></tr><tr><td>Eastern Central Asia (ECA)</td><td>1.196</td><td>1.095</td><td>1.077</td><td>0.45</td><td>1.124</td><td>0.685</td></tr><tr><td>Eastern Europe (EEU)</td><td>1.001</td><td>0.833</td><td>1.679</td><td>0.512</td><td>1.478</td><td>0.553</td></tr><tr><td>Eastern North America (ENA)</td><td>1.22</td><td>0.919</td><td>1.046</td><td>1.164</td><td>1.007</td><td>0.682</td></tr><tr><td>Eastern Siberia (ESB)</td><td>0.784</td><td>1.043</td><td>1.968</td><td>0.779</td><td>1.313</td><td>0.775</td></tr><tr><td><strong>Mediterranean (MED)</strong></td><td>1.099</td><td>0.566</td><td>1.721</td><td>0.603</td><td>1.448</td><td>0.644</td></tr><tr><td>North Central America (NCA)</td><td>1.079</td><td>1.013</td><td>1.018</td><td>0.472</td><td>1.292</td><td>0.257</td></tr><tr><td><strong>Northeastern North America (NEN)</strong></td><td>0.631</td><td>0.725</td><td>0.818</td><td>1.197</td><td>1.358</td><td>0.868</td></tr><tr><td>Northern Europe (NEU)</td><td>1.15</td><td>0.959</td><td>1.395</td><td>0.815</td><td>1.0</td><td>0.528</td></tr><tr><td>Northwestern North America (NWN)</td><td>0.924</td><td>0.645</td><td>0.723</td><td>0.416</td><td>0.975</td><td>0.45</td></tr><tr><td>Russian Far East (RFE)</td><td>0.972</td><td>1.099</td><td>1.87</td><td>0.615</td><td>1.738</td><td>0.76</td></tr><tr><td>South Asia (SAS)</td><td>1.04</td><td>0.957</td><td>1.373</td><td>1.097</td><td>1.891</td><td>1.154</td></tr><tr><td>Tibetan Plateau (TIB)</td><td>1.029</td><td>0.845</td><td>1.315</td><td>1.176</td><td>1.605</td><td>1.435</td></tr><tr><td><strong>Western Central Asia (WCA)</strong></td><td>1.076</td><td>0.538</td><td>1.336</td><td>0.474</td><td>1.314</td><td>0.71</td></tr><tr><td>Western North America (WNA)</td><td>1.028</td><td>0.973</td><td>1.203</td><td>0.53</td><td>0.971</td><td>0.643</td></tr><tr><td><strong>Western Siberia (WSB)</strong></td><td>0.763</td><td>0.714</td><td>1.697</td><td>0.669</td><td>1.401</td><td>0.532</td></tr><tr><td>West & Central Europe (WCE)</td><td>1.075</td><td>1.041</td><td>1.713</td><td>0.687</td><td>1.14</td><td>0.629</td></tr></tbody></table>
<table><caption>Table A7. Odds ratio of different regions for LAI<sub>low</sub>. Corresponding winter preconditioned regions are in bold.</caption><thead><tr><th>IPCC region (abbreviation)</th><th>T<sub>w</sub></th><th>P<sub>w</sub></th><th>T<sub>sp</sub></th><th>P<sub>sp</sub></th><th>T<sub>su</sub></th><th>P<sub>su</sub></th></tr></thead><tbody><tr><td>Central North America (CNA)</td><td>1.292</td><td>0.797</td><td>1.208</td><td>0.939</td><td>1.13</td><td>0.836</td></tr><tr><td>East Asia (EAS)</td><td>1.297</td><td>1.0</td><td>1.307</td><td>1.001</td><td>1.067</td><td>1.276</td></tr><tr><td>Eastern Europe (EEU)</td><td>0.941</td><td>1.108</td><td>1.017</td><td>0.946</td><td>0.813</td><td>0.99</td></tr><tr><td>Eastern North America (ENA)</td><td>0.906</td><td>1.061</td><td>1.257</td><td>0.813</td><td>0.86</td><td>1.069</td></tr><tr><td>Eastern Siberia (ESB)</td><td>0.786</td><td>0.868</td><td>1.107</td><td>0.912</td><td>0.801</td><td>1.078</td></tr><tr><td>Mediterranean (MED)</td><td>1.038</td><td>0.748</td><td>1.815</td><td>0.717</td><td>1.324</td><td>0.659</td></tr><tr><td>North Central America (NCA)</td><td>0.974</td><td>0.801</td><td>0.858</td><td>0.776</td><td>1.988</td><td>0.821</td></tr><tr><td>Northeastern North America (NEN)</td><td>0.903</td><td>0.615</td><td>1.476</td><td>0.862</td><td>0.434</td><td>0.928</td></tr><tr><td>Northern Europe (NEU)</td><td>1.063</td><td>0.82</td><td>0.962</td><td>0.808</td><td>0.625</td><td>0.985</td></tr><tr><td>Northwestern North America (NWN)</td><td>0.943</td><td>0.858</td><td>1.05</td><td>0.755</td><td>0.448</td><td>0.78</td></tr><tr><td>Russian Arctic (RAR)</td><td>0.792</td><td>0.703</td><td>1.073</td><td>0.824</td><td>0.475</td><td>0.897</td></tr><tr><td>Russian Far East (RFE)</td><td>0.964</td><td>0.725</td><td>0.975</td><td>1.007</td><td>0.599</td><td>0.911</td></tr><tr><td>South Asia (SAS)</td><td>0.867</td><td>1.108</td><td>1.575</td><td>1.085</td><td>1.186</td><td>1.611</td></tr><tr><td>Tibetan Plateau (TIB)</td><td>0.922</td><td>0.946</td><td>1.242</td><td>0.985</td><td>1.084</td><td>1.613</td></tr><tr><td>Western Central Asia (WCA)</td><td>1.14</td><td>0.64</td><td>1.261</td><td>0.705</td><td>1.129</td><td>0.702</td></tr><tr><td>Western North America (WNA)</td><td>0.983</td><td>0.814</td><td>1.006</td><td>0.62</td><td>0.889</td><td>1.069</td></tr><tr><td>Western Siberia (WSB)</td><td>0.704</td><td>0.893</td><td>1.146</td><td>0.905</td><td>0.725</td><td>1.08</td></tr><tr><td>West & Central Europe (WCE)</td><td>1.294</td><td>1.062</td><td>1.121</td><td>0.766</td><td>0.955</td><td>1.063</td></tr></tbody></table>

<table><caption>Table A8. Odds ratio of different regions for LAI<sub>high</sub>. Corresponding winter preconditioned regions are in bold.</caption><thead><tr><th>IPCC region (abbreviation)</th><th>T<sub>w</sub></th><th>P<sub>w</sub></th><th>T<sub>sp</sub></th><th>P<sub>sp</sub></th><th>T<sub>su</sub></th><th>P<sub>su</sub></th></tr></thead><tbody><tr><td>Central North America (CNA)</td><td>0.916</td><td>1.012</td><td>1.163</td><td>0.977</td><td>0.9</td><td>1.441</td></tr><tr><td>East Asia (EAS)</td><td>0.719</td><td>0.755</td><td>1.125</td><td>1.153</td><td>0.716</td><td>1.077</td></tr><tr><td>Eastern Central Asia (ECA)</td><td>0.856</td><td>1.149</td><td>1.053</td><td>1.706</td><td>0.914</td><td>1.557</td></tr><tr><td>Eastern Europe (EEU)</td><td>0.923</td><td>1.087</td><td>0.647</td><td>1.8</td><td>0.614</td><td>1.334</td></tr><tr><td>Eastern North America (ENA)</td><td>0.739</td><td>1.147</td><td>1.186</td><td>0.813</td><td>1.155</td><td>1.59</td></tr><tr><td>Eastern Siberia (ESB)</td><td>0.968</td><td>0.8</td><td>1.002</td><td>1.433</td><td>0.798</td><td>1.493</td></tr><tr><td>Mediterranean (MED)</td><td>0.973</td><td>1.255</td><td>0.601</td><td>1.704</td><td>0.618</td><td>1.37</td></tr><tr><td>North Central America (NCA)</td><td>0.607</td><td>1.318</td><td>1.293</td><td>1.667</td><td>0.461</td><td>1.707</td></tr><tr><td>Northeastern North America (NEN)</td><td>0.984</td><td>1.194</td><td>0.936</td><td>1.004</td><td>1.307</td><td>1.354</td></tr><tr><td>Northern Europe (NEU)</td><td>1.556</td><td>0.82</td><td>0.487</td><td>1.198</td><td>0.733</td><td>1.285</td></tr><tr><td>Northwestern North America (NWN)</td><td>1.584</td><td>1.26</td><td>0.783</td><td>1.283</td><td>0.819</td><td>1.39</td></tr><tr><td>Russian Far East (RFE)</td><td>1.049</td><td>0.875</td><td>1.09</td><td>0.961</td><td>1.201</td><td>0.983</td></tr><tr><td>South Asia (SAS)</td><td>0.729</td><td>0.947</td><td>0.938</td><td>1.223</td><td>0.736</td><td>1.071</td></tr><tr><td>Tibetan Plateau (TIB)</td><td>0.569</td><td>0.754</td><td>1.001</td><td>1.171</td><td>0.823</td><td>0.753</td></tr><tr><td>Western Central Asia (WCA)</td><td>0.893</td><td>1.478</td><td>0.864</td><td>1.642</td><td>0.784</td><td>1.231</td></tr><tr><td>Western North America (WNA)</td><td>1.042</td><td>1.04</td><td>0.81</td><td>1.545</td><td>0.996</td><td>1.464</td></tr><tr><td>Western Siberia (WSB)</td><td>1.548</td><td>0.992</td><td>0.671</td><td>1.05</td><td>0.788</td><td>1.817</td></tr><tr><td>West & Central Europe (WCE)</td><td>0.941</td><td>1.094</td><td>0.651</td><td>1.35</td><td>0.791</td><td>1.34</td></tr></tbody></table>
<table><caption>**Table A9.** Odds ratio of different regions for LAI<sub>high</sub>. Corresponding winter preconditioned regions are in bold.</caption><thead><tr><th>IPCC region (abbreviation)</th><th>T<sub>w</sub></th><th>P<sub>w</sub></th><th>T<sub>sp</sub></th><th>P<sub>sp</sub></th><th>T<sub>su</sub></th><th>P<sub>su</sub></th></tr></thead><tbody><tr><td>Central North America (CNA)</td><td>0.858</td><td>0.978</td><td>0.9</td><td>0.966</td><td>0.936</td><td>1.306</td></tr><tr><td>East Asia (EAS)</td><td>0.87</td><td>0.909</td><td>0.924</td><td>1.111</td><td>1.102</td><td>0.933</td></tr><tr><td><strong>Eastern Europe (EEU)</strong></td><td>1.14</td><td>1.269</td><td>1.022</td><td>1.061</td><td>1.269</td><td>0.768</td></tr><tr><td><strong>Eastern North America (ENA)</strong></td><td>0.839</td><td>0.89</td><td>0.805</td><td>1.011</td><td>1.115</td><td>0.984</td></tr><tr><td><strong>Eastern Siberia (ESB)</strong></td><td>1.293</td><td>1.087</td><td>1.117</td><td>1.198</td><td>1.25</td><td>0.962</td></tr><tr><td>Mediterranean (MED)</td><td>1.073</td><td>1.092</td><td>0.658</td><td>1.412</td><td>0.67</td><td>1.366</td></tr><tr><td>North Central America (NCA)</td><td>0.958</td><td>0.842</td><td>0.845</td><td>1.37</td><td>0.514</td><td>1.247</td></tr><tr><td>Northeastern North America (NEN)</td><td>0.878</td><td>1.028</td><td>0.676</td><td>1.245</td><td>1.95</td><td>1.247</td></tr><tr><td><strong>Northern Europe (NEU)</strong></td><td>1.21</td><td>1.311</td><td>0.862</td><td>1.323</td><td>1.782</td><td>0.993</td></tr><tr><td>Northwestern North America (NWN)</td><td>0.933</td><td>1.334</td><td>0.891</td><td>1.309</td><td>1.718</td><td>1.288</td></tr><tr><td><strong>Russian Arctic (RAR)</strong></td><td>1.131</td><td>1.385</td><td>1.307</td><td>1.124</td><td>2.05</td><td>1.058</td></tr><tr><td>Russian Far East (RFE)</td><td>1.053</td><td>1.186</td><td>1.215</td><td>0.978</td><td>1.476</td><td>0.95</td></tr><tr><td>South Asia (SAS)</td><td>1.12</td><td>0.957</td><td>1.02</td><td>1.228</td><td>0.888</td><td>0.742</td></tr><tr><td>Tibetan Plateau (TIB)</td><td>1.039</td><td>0.777</td><td>0.745</td><td>1.061</td><td>0.731</td><td>0.707</td></tr><tr><td>Western Central Asia (WCA)</td><td>1.046</td><td>1.269</td><td>0.94</td><td>1.175</td><td>0.715</td><td>1.244</td></tr><tr><td>Western North America (WNA)</td><td>1.114</td><td>1.15</td><td>0.81</td><td>1.369</td><td>0.998</td><td>1.111</td></tr><tr><td>Western Siberia (WSB)</td><td>1.26</td><td>1.123</td><td>1.091</td><td>0.94</td><td>1.473</td><td>0.962</td></tr><tr><td>West & Central Europe (WCE)</td><td>0.919</td><td>0.999</td><td>0.851</td><td>1.203</td><td>0.931</td><td>0.993</td></tr></tbody></table>

## ORCID iDs

Mohit Anand https://orcid.org/0000-0002-1688-263X

Raed Hamed https://orcid.org/0000-0003-2243-3109

Patrícia S Silva https://orcid.org/0000-0003-0410-2971

Freya K Garry https://orcid.org/0000-0002-9640-6675

Ana Bastos https://orcid.org/0000-0002-7368-7806

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