# Uneven impacts of climate change around the world and across the annual cycle of winegrapes

E. M. Wolkovich, Benjamin I. Cook, Iñaki García de Cortázar-Atauri, Victor van der Meersch, Thierry Lacombe, Cécile Marchal, Ignacio Morales-Castilla

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E. M. Wolkovich, Benjamin I. Cook, Iñaki García de Cortázar-Atauri, Victor van der Meersch, Thierry Lacombe, et al.. Uneven impacts of climate change around the world and across the annual cycle of winegrapes. PLOS Climate, 2025, 4 (5), pp.e0000539. 10.1371/journal.pclm.0000539. hal-05120326

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RESEARCH ARTICLE

# Uneven impacts of climate change around the world and across the annual cycle of winegrapes

E. M. Wolkovich ID 1*, Benjamin I. Cook 2,3, Iñaki García de Cortázar-Atauri ID 4, Victor Van der Meersch ID 1, Thierry Lacombe ID 5, Cécile Marchal 6, Ignacio Morales-Castilla ID 7

1 Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada, 2 NASA Goddard Institute for Space Studies, New York, New York, United States of America, 3 Lamont-Doherty Earth Observatory, Palisades, New York, United States of America, 4 INRAE, US 1116 Agroclim, Avignon, France, 5 AGAP Institut, University of Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, France, 6 Vassal-Montpellier Grapevine Biological Resources Center, INRAE, Marseillan-Plage, France, 7 GloCEE - Global Change Ecology and Evolution Group, Department of Life Sciences, University of Alcalá, Alcalá de Henares, Spain

* e.wolkovich@ubc.ca

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OPEN ACCESS

**Citation:** Wolkovich EM, Cook BI, García de Cortázar-Atauri I, Van der Meersch V, Lacombe T, Marchal C, et al. (2025) Uneven impacts of climate change around the world and across the annual cycle of winegrapes. PLoS Clim 4(5): e0000539. https://doi.org/10.1371/journal.pclm.0000539

**Editor:** Noureddine Benkeblia, University of the West Indies, JAMAICA

**Received:** November 13, 2024

**Accepted:** April 04, 2025

**Published:** May 21, 2025

**Peer Review History:** PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pclm.0000539

**Copyright:** © 2025 Wolkovich et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Data Availability Statement:** This manuscript uses already publicly available data from BEST

## Abstract

Anthropogenic climate change has uneven impacts across the globe and throughout the year. Such unevenness poses a major challenge for human adaptation, especially for agricultural and other managed systems. Estimating effects beyond one region is challenging, however, because differences between regions make it difficult to know what seasonal periods of climate to compare. Both local climate and the planting decisions of a region affect the relevant periods for estimating changes in climate. Here, we use recent phenological models with a dataset of mean phenology for over 500 cultivars (varieties) to estimate climatic changes in growing regions across the globe for a major perennial crop that has been highly affected by climate change: winegrapes. We examine a suite of grower-relevant metrics, including temperatures during budburst, throughout the growing season and temperatures and precipitation surrounding harvest. We find that climate change has impacted all regions, especially for heat metrics across the full growing season (GDD, maximum temperature and days above 35 °C). By far the largest shifts, however, are in European regions, where the number of hot days (>35 °C) and maximum growing season temperatures are several standard deviations higher than before significant anthropogenic climate change. Including variety diversity in our estimates impacted only metrics at the start and end of the season, appearing most important for harvest-related climate metrics, and then only in 'Old World' regions, where most variety diversity is planted. Climate change impacts have thus been highly uneven across the world's winegrowing regions and the impacts are variable across the growing season. Navigating how best to adapt the global winegrowing industry to climate change will require addressing these spatial and temporal complexities.
(https://berkeleyearth.org/data/), variety
planting (https:
//www.adelaide.edu.au/press/titles/winegrapes)
and data from INRAE Domaine de Vassal Grape
Collection on phenology
(https://bioweb.supagro.inra.fr/collections_
vigne/Home.php?l=EN). We also have made the
exact data underlying the figures and tables
available at: https:
//knb.ecoinformatics.org/view/urn%3Auuid%
3A3cf715e3-24d1-4a41-8632-dc51a31a1011.

**Funding:** This project was supported in part by Natural Sciences and Engineering Research Council of Canada Discovery Research Program (RGPIN-2018-05038 to EMW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Spanish Ministry for Science and Innovation (grant number PID2023-1523290B-100 to I.M.-C.)

**Competing interests:** The authors have
declared that no competing interests exist.

# 1. Introduction

Climate change's impacts on agriculture are some of the most well-documented [1,2], and consequential, given their relevance to food and economic security [3]. Documented changes in phenology and yield are now prevalent, with growing reports of damage from climatic events. Yet, how best to adapt agriculture to a changing climate requires understanding the relative impacts across a growing season.

These shifting stressors on crops depend strongly on crop phenology, as risks from low temperatures often increase near budburst and higher temperatures may be most dangerous during flowering and fruiting [4,5]. Yet predicting crop phenology is complex, especially when integrating variation due to local climates and planting decisions. As such, most current studies of climate impacts have focused on smaller geographic regions. Such efforts give important insights, relevant to the region or country-level, but limit a larger understanding of how similarly—or not—climate change affects a crop across different regions, and across the growing season. Winegrapes provide a potentially useful case study to examine impacts across regions and the annual calendar of a perennial crop. Given their high economic importance, research into how current and projected climate change will impact the horticultural crop of winegrapes is especially common. While global reports have predicted dramatic shifts in what areas are suitable—or not—for winegrowing [e.g. [6–8]], country-level reports have tackled finer-scale analyses [e.g. [9–12]]. Predicted shifts in winegrowing regions are already well underway. Winegrowing in more poleward regions had increased in hectares and/or the diversity of varieties planted [13,14]. At the same time current growing regions have consistently documented earlier harvests and higher sugar levels, as warming speeds up the development cycle into hotter periods that shift the balance of sugar:acid in fruit [15–18].

Despite this growing literature, we lack a global view of how climate change has impacted winegrowing regions. This could be due in part to the complexity of winegrowing itself. Different varieties vary strongly in their phenology, and each region plants a distinct mix of varieties, with over 1,100 currently planted globally [19,20]. Further, winegrowing regions—though generally limited to between 35–50 °N and 30–45 °S [17]—vary strongly in their distribution, spanning five continents and diverse terrain. This makes finding climatic data consistent enough to compare changes across different regions challenging. Indeed it is the unique areas where winegrapes are grown, combined with the varieties planted, that make up much of the definition of terroir—and determine in part wine quality [21,22]. Despite these challenges, increases in globally resolved climate data, global planting data of winegrape varieties and new variety-specific models of winegrape phenology make an initial study of how climate change has changed the world's terroir possible.

Here, we use winegrapes as a case study to understand how climate stressors have shifted globally across the annual plant cycle. Using newly available data and models, we test how 10 grower-relevant climate metrics have shifted—from minimum temperatures during dormancy and around budburst, to heat extremes over the growing season, and through to temperatures and precipitation during harvest. We compare results from metrics treating all regions as growing the same variety, versus integrating the diversity of varieties actually grown by combining variety-specific phenological models with data on the relative phenologies of over 500 varieties. Given our aim of a global comparison based on metrics that require daily climate data (generally only present at fine resolution for specific regions), our spatial scale is very coarse (100 km), but our results provide the first insight into the new reality of grape growing in the Anthropocene [defined following [23]].
## 2. Methods

We developed climate metrics for the world's winegrowing regions that spanned the annual plant cycle of a calendar year (Fig 1). We considered a number of important indicators, broken down into major categories. For aggregators across the growing season we estimated, from budburst to harvest, the mean temperature ($T_{mean}$) and growing degree days (GDD, growing degree days calculated with base temperature of 10 °C). We also examined (from budburst to harvest) high temperatures through three metrics: the highest maximum temperature ($T_{max}$), the number of days maximum temperatures were above 35 °C ($T_{max35}$) and the growing degree days calculated with base temperature of 30 °C ($GDD_{30}$). As harvest conditions can be important to winegrape quality [4,21], we considered the minimum temperature, calculated from 30 days before harvest ($T_{minharvest}$), total precipitation in the month of harvest ($prec_{harvest}$) and total precipitation in the month of and the month before harvest ($prec_{harvest2mon}$). Finally, we included several metrics related to low temperatures during the winter and early spring: the lowest minimum temperature during the dormancy period (calculated after harvest until before budburst, $T_{mindorm}$), the lowest minimum temperature in the

<figure><img src="image_4.png" /><figcaption>**Fig 1.** We developed 10 climate metrics that span the annual cycle of winegrowing. See Methods for more details on each metric.</figcaption></figure>

https://doi.org/10.1371/journal.pclm.0000539.g001
months near budburst ($T_{minbudburst}$, calculated between 30 days before and 30 days after the budburst date) and the number of days from 30 days before and 30 days after budburst when the minimum temperature is below -6.7 °C ($T_{min-6.7}$).

We report all but three of these metrics in the main text. We found that non-zero values of $GDD_{30}$ and $T_{min-6.7}$ were rare across most regions. Precipitation in month of harvest plus month before was very similar to precipitation in the month of harvest. Therefore, we show these three metrics ($GDD_{30}$, $T_{min-6.7}$, $prec_{harvest2mon}$) only in the Supporting Information (Fig A in S1 Text).

To adjust for phenology in each region, we estimated budburst and veraison using global planting data for the world's winegrowing regions [19,24] combined with variety-specific phenological models [see [7] for complete details]. These models are based on phenological data from Europe (mainly France and Germany) where humidity levels are higher and irrigation less common than other regions; while these models performed well on out-of-sample data from irrigated vines in Napa and Davis, California [see [7]], they may be less accurate for many regions with differing climates and management regimes. Our procedure (Fig B in S1 Text) starts by using these models to characterize average budburst and veraison dates in each of 620 unique regions (Fig 2). Averages were computed across projected phenologies for each historical year (1970-1979) and 12 varieties: 11 common varieties (Cabernet-Sauvignon, Chardonnay, Chasselas, Garnacha tinta, Merlot, Monastrell, Pinot noir, Riesling, Sauvignon blanc, Syrah, Ugni blanc) that represent 34.3% of all planted hectares, and one late-ripening composite simulated variety (LRC, made up of Fogarina, Lambrusco di Sorbara, Savvatiano, Verdelho tinto, Brun argenté, Amaral, Uva cão, Verdicchio bianco). The latter eight varieties that make up the LRC represent only 0.3% of planted hectares.

Following, we estimated climate metrics based on two different variety scenarios (using the projected phenologies of the above mentioned varieties, see also Fig B in S1 Text). First, we estimated metrics as though all regions planted Pinot noir [similar to [9]]—henceforth referred to as 'no variety diversity' estimates. We selected Pinot noir because it is the most

<figure><img src="image_5.png" /><figcaption>**Fig 2.** Locations of winegrowing regions included in our analyses, grouped by the United Nations geographical subregions. The base layer of the map comes from Natural Earth, a site whose data are public domain (see https://www.naturalearthdata.com/about/terms-of-use/).</figcaption></figure>

https://doi.org/10.1371/journal.pclm.0000539.g002
widely-planted early-ripening red grape variety (Fig C in S1 Text), and thus relevant to many regions and because it is early ripening. Cabernet-Sauvignon and Merlot are planted in slightly more regions according to [24], but in our models these varieties did not reach harvest in two times more regions than Pinot noir. Thus Pinot noir represented a compromise between a widely-grown variety and one for which we could estimate metrics in most regions. Second, we estimated climate metrics adjusting—as possible—for the different plantings of each region. Based on [24], we estimated geographical locations for all regions and synonymized varieties [more details in [19]]. To estimate budburst and veraison for all possible varieties within a region, we used the projected historical dates for the 11 common varieties, for all of the eight varieties used to build the LRC, and for a combined approach where we characterized the phenology of other varieties relative to standardized phenological data from the INRAE Domaine de Vassal Grape Collection (see Fig B in S1 Text for details on this approach). Domaine de Vassal has recorded phenological data for hundreds of varieties, standardized to the common variety of Chasselas [25], a relatively early-ripening variety (i.e., phenological event dates are recorded as the number of days relative to Chasselas for that year).

Of 1237 unique variety names in Anderson data, we matched 562 to Domaine de Vassal code names. These unmatched varieties represented 11% of all hectares (of which 5.2% were ‘Other Red’ and ‘Other White’ so could not be matched, the next top non-matched varieties were Isabella and Concord—most often used as table grapes, suggesting we captured most major winegrape varieties). After synonymizing all varieties for which we had Domaine de Vassal data (Vassal below) we estimated budburst and veraison dates, standardized with respect to data from Domaine de Vassal. For example, to estimate budburst (budburst, BB) for any given variety in a given region: first, we projected historical dates (Proj below) for a well-measured later variety (Cabernet-Sauvignon, Std1 below) and a well-measured early variety (Chasselas, Std2). Second, we calculated the mean of projected budburst for Std1 and Std2. Third, we obtained the recorded distance between the budburst of our target variety (BBVassalVariety) and Std1 (BBVassalStd1) and Std2 (BBVassalStd2), according to the Domaine de Vassal dataset. Finally, we estimate a budburst date, using:

$$BB_{\text{Variety}} = BB_{\text{ProjStd1}} - \frac{(BB_{\text{ProjStd1}} - BB_{\text{ProjStd2}})(BB_{\text{VassalStd1}} - BB_{\text{VassalVariety}})}{(BB_{\text{VassalStd1}} - BB_{\text{VassalStd2}})} \quad (1)$$

We used the same equation for veraison. We estimated harvest as veraison +45 days [as explained in [7]]; excluding a small number of specific years for certain varieties with certain regions when harvest did not occur within 365 days after budburst.

With these estimates of average budburst and harvest for all varieties in all regions, we extracted a suite of climate metrics using daily climate data from 1951-2018. Given our aim of a global comparison based on metrics that require daily climate data, we used two well established and widely used climate datasets: BEST (Berkeley Earth Surface Temperatures) data for temperature [version as of February 2022, [26]], and GPCC for monthly precipitation [27]. Previous studies found BEST gave similar results to more local-scale climate data when calibrating winegrape models to vineyard-level phenological data from regions in Europe and North America [7]. Still, because of the coarse spatial scale of our data we excluded regions at high elevations (over 2000 m at our gridcell scale, excluding 15 of 620 unique regions). We averaged by gridcell for before and after 1980, (unweighted, as weighting by planted hectares had limited effect, see Fig D in S1 Text), then averaged gridcell estimates for country and larger region-level estimates, using the United Nations geographical subregions. Our final dataset included 500 winegrowing regions across 322 gridcells (Fig 2, see also S1 Text).
We also report results using daily gridded temperatures from ERA5, a reanalysis dataset from the European Centre for Medium-Range Weather Forecasts [ECMWF, [28]]. Unlike BEST, which generates gridded estimates of daily temperatures based on statistical interpo- lations from surface observations, ERA5 is a data assimilation product. ERA5 uses a physi- cal model where the three-dimensional simulated meteorological fields are “nudged” using a diverse set of observations from the surface, upper atmosphere, and satellites. BEST and ERA5 may therefore have some significant differences, especially at local scales where the quality and quantity of observations can differ substantially between the two products. We focus on the results from the BEST analysis in the main text, based on previous successful applica- tions for wine phenology discussed above, but present full results from both datasets in the Supporting Information.

We report averaged metrics generally as means and standard deviations in both natural units (e.g., °C, days, mm) and standardized units to compare climate metrics and regions more easily. To standardize we calculated the mean and standard deviation (SD) before 1980 for each gridcell then used those values to standardize all years since 1980 (resulting in z-scores standardized to local pre-1980 conditions). We considered 1980 as a relevant point to define pre- and post anthropogenic climate change based on global analyses [29,30]. To assess how accounting for variety diversity altered our estimated metrics, we calculated the root mean square error (RMSE) resulting from comparing each climate metric computed with and without diversity.

## 3. Results

For all climate metrics, estimates were highly similar whether we included variety diversity in our phenological estimates, or treated all regions (and hectares) as growing the same wine-grape variety (see Fig 3), save for a slight increase in growing season GDD in regions with high GDD and small effects in some harvest-related metrics. For these metrics, variety diversity tended to increase the minimum temperature at harvest and increase the precipitation (bottom row of Fig 3), though generally only in Western/Southern Europe. Based on this, we report the remaining results using the metrics without variety diversity included, but present results for both in the Supporting information (Tables A–V in S1 Text). Average mean growing season temperatures increased across regions of the world 1.3 SD units, ranging from 0.8 in Central/Eastern/South-eastern Asian to 1.8 in Northern African/Western Asian, leading to a similar scale of SD shifts in GDD (Fig 4). These SD unit shifts translate into average gains of 96 GDD, with a high of 159 GDD in Western/Southern European (Fig 5).

These changes were dwarfed by shifts in high temperatures—especially in Europe—where maximum growing season temperatures have increased 2.1 SD units in Eastern Europe and 2 SD units in Western Europe; representing increases of 2.9 and 2.5 °C, respectively (Figs 4 and 5). Shifts in days expected to be too hot for growth and development in non-irrigated areas—days > 35 °C—show even higher shifts: 6 SD units higher in Eastern Europe and 4.6 SD units higher in Western Europe. These translate into increases of 0.6 and 1.1 days per growing season, as such types of climatic events were rare before 1980 (thus apparently small increases represent large SD increases).

Increases in minimum temperatures during winter dormancy, budburst and harvest were comparatively smaller. All three metrics showed warming of approximately 1 °C (0.9 for winter, 0.8 for budburst and 1 for harvest; or 0.6 for winter, 0.7 for budburst and 0.8 for harvest in SD units). The largest increases occurred in Central/Eastern/South-eastern Asian for minimum winter temperatures, with shifts of 1.2 SD units or 2.2 °C.
<figure><img src="image_6.png" /><figcaption>Fig 3. Comparisons of climate metrics (in natural units: °C, days or mm) assuming a single variety's phenology (Pinot noir) for all regions (horizontal-axis), versus adjusting by a region's variety plantings (vertical-axis), colored by world regions (as shown in Fig 2), using climate data from 1980 onward. We show the 1:1 line and the root mean square error (RMSE, also in natural units).</figcaption></figure>

https://doi.org/10.1371/journal.pclm.0000539.g003
<figure><img src="image_7.png" /><figcaption>Fig 4. Shifts in climate metrics in standardized (standard deviation—SD) units after significant anthropogenic climate change (1980–2018) standardized to a baseline of 1950–1979 climate (gray line at 0). Means (dots) and 1 SD (lines) shown.</figcaption></figure>

https://doi.org/10.1371/journal.pclm.0000539.g004
<figure><img src="image_8.png" /><figcaption>Fig 5. Comparisons of climate metrics in natural units (°C, days or mm) before (1950–1979) and after significant anthropogenic climate change (1980–2018) for major metrics, colored by world regions (as show in Fig 2). For a version showing only means and SD see Fig 2.</figcaption></figure>

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Precipitation in the months before harvest did not change on average (0.04 SD units, Fig 4), though some regions saw small decreases or increases. Eastern/Southern African decreased -0.2 SD units (-3.8 mm), while Eastern European increased 0.3 SD units (7.6 mm).

While all regions saw increases in most temperature metrics (mean, GDD, minimum temperatures in the winter, near budburst and harvest) most regions have a unique mix of shifting climate metrics. For example, mean temperature increases were similar in Northern Africa/Western Asian regions (1.8 SD units) and Southern/Western Europe (1.7 SD units) which translated into similar shifts in GDD (1.8 and 1.7, respectively) but for Northern Africa/Western Asia—already one of the warmest growing regions (20.8°C and 1716 GDD, before 1980) these changes led to much smaller shifts in high temperature and minimum temperature metrics, whereas in comparatively cooler Southern/Western Europe (17.4°C and 1258 GDD, before 1980) warming has grossly increased high temperatures (Figs 4 and 5).

## 4. Discussion

Using a unique global perspective we found uneven impacts of anthropogenic climate change across the annual cycle of one major perennial crop—winegrapes—and across growing regions spanning five continents. While warming generally increases minimum more than maximum temperatures [2], we found shifts in grower-relevant metrics related to minima (e.g., low temperatures during budburst, harvest and during dormancy) were generally smaller than metrics related to heat and high temperatures. Globally, the largest shifts related to increases in heat across the growing season, with many regions having average temperatures and total GDD across the growing season one or more standard deviation higher since 1980, when anthropogenic warming began to accelerate [29]. Metrics of heat extremes (maximum temperatures and days above 35°C) for regions in Western/Southern Europe and in Eastern Europe showed by far the largest increases (2 to 4 standard deviations) and suggest a new world of European winegrowing that has few parallels to European winegrowing of 40-50 years before.

Globally, warming has reshaped the climate of winegrape's annual cycle, but the exact constellation of how metrics have shifted highlights the geographical variability of climate change. Europe's northern latitudinal location (which is higher than most other northern hemisphere winegrowing regions) positions it for more extreme warming overall as northern latitudes warm the most [29], with increases in summer heat potentially driven by shifts in atmosphere-ocean circulation patterns and reduced anthropogenic aerosol emissions [31–33]. In contrast, our results for South America show similar increases to Europe in mean temperatures and GDD but few increases in extreme heat (though our results do not include most mountainous regions, further discussion below in 'Human adaptation of agricultural systems to uneven warming across scales').

How much these shifts impact regions, however, depends on how warm—or dry—they were before recent climate change. Regions already at the hotter extremes possible for grapes (e.g., Northern Africa) may experience high impacts of even small increases in temperatures or related disturbances such as increases in wildfires (e.g., Australia), which destroy harvests through loss of vines or smoke damage [8]. Shifts in precipitation could also disproportionately impact how much warming affects winegrapes. Though we found only small changes in our one precipitation-related metric (precipitation in the month of harvest), they could have large effects [8].

To date, most grower-relevant metrics for winegrapes relate to the concern of having too much—not too little—precipitation during harvest, which can reduce quality [34]; this focus is likely because many regions irrigate during droughts [16], but this may change as droughts
increase with warming [35]. Recent reports of yield declines in Spain and Italy (where irrigation is historically less common) cited droughts as a cause. Thus, similar to how increases in heat may affect the warmest regions the most, drought in the driest regions may have the largest impacts—with the Mediterranean one of the most robustly drying regions due to climate change [36]. The interaction of warming and a region's aridity further matters, as recent work suggests rising temperatures in dry regions will accelerate local warming, while regions with higher absolute humidities will experience increasing humidity [37,38], with likely increases in mildew pressure. This in turn will likely make choices of which grape varieties to grow increasingly important.

**4.1. Influence of cultivar diversity on climate change impacts**

Multiple lines of evidence suggest that shifting to longer-ripening, more heat- and drought-tolerant varieties (cultivars) of winegrapes should build resilience in winegrowing [e.g., [7,8,16]]. However, considering the phenology of over 500 cultivars, we found few differences in climate metrics derived from considering a region's planted winegrapes versus assuming all regions plant 100% Pinot noir. These results reflect in part one reality of winegrowing today: most regions plant a highly limited diversity of winegrapes. Because most are dominated by a limited set of 'international' varieties, missing early-ripening and—even more so—late ripening varieties [19], our data on 500 cultivars rarely changed metrics from such regions. Only European regions include large portions of late ripening cultivars, which is the only region where any differences due to variety diversity were apparent. Such differences may become more apparent as climate change continues, and makes the match—or mismatch—between climate and variety more apparent [8].

Our findings of little effect of plant winegrape diversity are also likely due in part to the climate metrics we considered. We selected metrics that are common, widely-used and grower-relevant. All these metrics, however, are either focused on integrating over long periods of the annual cycle (growing season or dormancy) or focus on metrics at the start or end of the growing season. Because most winegrape varieties will share a common growing season from spring to summer that varies depending only a variety's budburst and harvest timing, impacts of a region's winegrape diversity would mainly appear in metrics related to the start or end of the growing season, as we found. Varieties also vary strongly, however, in their flowering and veraison [25] and we expect larger effects of a region's diversity with metrics related to these two events. Though most currently used bioclimatic indicators for winegrapes do not include such metrics, they are clearly critical to crop yield and quality—and to how grapes perform during climate extremes [39]. Thus considering mid-season phenological events appears increasingly relevant when making planting decisions, and understanding climate change impacts.

Estimating the importance of cultivar diversity is further limited by available data. While analyses repeatedly suggest that phenological diversity is critical for resilient global wine-growing with climate change, previous analyses generally have not included it [e.g., [11,40]] or included a very limited slice of real diversity [up to 12 varieties, e.g., [6,7]] due to the challenge of obtaining sufficient phenological data to calibrate models. Our approach leveraged phenological models with global data on cultivar plantings and an unparalleled research collection of varieties to overcome this challenge, including the phenological diversity of over 500 different varieties. Yet this still represents less than half the planted diversity [20,24] because phenological data were not available for all varieties, and—to a lesser extent—due to synonymy issues in matching variety names, which often could not be positively matched to
known names. Improved models for winegrapes could come from planting data [20,41] using
internationally standardized names [42] and more phenological data.

## 4.2. Human adaptation of agricultural systems to uneven warming across scales

Our results highlight the complexity of human adaptation of one major agricultural system to climate change, due to varying impacts across the globe and calendar year, but also due to data limitations that may hamper efforts to capture a global perspective of impacts, let alone adaptation options [43,44]. High quality global climate data—even for a variable as common as temperature—is notoriously difficult to develop given many factors, including how temperatures vary across complex landscapes and the high unevenness in weather stations across regions [45].

Here we used a repeatedly verified and updated dataset with one of the finest spatial res-
olutions for daily data [45]—BEST [Berkeley Earth Surface Temperatures, [26]]—yet the
resolution (1°) is still incredibly large for a crop as sensitive to climate and topography as
winegrapes. To limit some of the downsides of this scale, we omitted high elevation regions
(e.g., Mendoza, Argentina) that may be especially poorly represented by large gridded cli-
mate datasets, and require analyses highly specific to their terrain. Much finer scale data are
available (though potentially not at the scale to deal with regions extreme in their orography),
but only for specific smaller regions [e.g., E-OBS for Europe, [46]] or for aggregated variables
(e.g., monthly temperatures) and rarely before 1980 but see [28] and *Methods and results using
ERA5 data* in the S1 Text], which likely explains why so many impact studies to date have
been comparatively limited in their spatial scale [e.g. [11,15,47]].

Global efforts to adapt crops to climate change benefit from a global perspective, as one
region may serve as a sentinel or test-case for other regions, with knock-on benefits for adap-
tation. Our analyses highlight that Europe has experienced the most severe shift in summer
heat—with GDD, days above 35 °C, and maximum temperatures most years now far out-
side of what was experienced before significant warming. Impacts of this new, hotter climate
include lower grape yields, heat damage to berries and vegetation [8,39,48] and an industry
that is rapidly working to adapt [16]. Certainly large commercial growers use global analyses
to determine where to potentially shift their crop lands, but such analyses are also important
for growers working to adapt in place.

As growers test new methods to adapt in place—from shade cloth to new rootstocks and varieties—and discover what works, other regions may then have a clearer path towards adaptation when they experience similar summer warming [e.g., [48,49]]. Leveraging research and trials across regions is already underway. For example, widespread fire impacts on winegrapes that began first in Australia led to technologies and approaches that could be deployed quickly as California and other western North America winegrowing regions began experiencing similar fires [50], though more local differences (e.g., in forest composition) make some adaptations less transferrable [16]. In such cases, and more generally, sharing perspectives regionally—for example, heat adaptations in Italy or Spain that may benefit French winegrowing—may be especially valuable. Our analyses in part showcase this reality through the differences captured by comparing shifts in climate on a relative scale (how much a climate metric has shifted relative to a pre-climate change baseline) versus on an absolute scale (e.g., °C or days). For example, while Europe has experienced by far the largest relative increases in summer heat, many European regions are still cooler than most North African, Asian and Australian regions. Thus, while the changes in Europe are likely experienced as
a very dramatic shift for growers, growers in other regions are contending with much more extreme heat for grape growing.

Winegrowing has always historically taken a geographical perspective—through appellations and sub-appellations that variously delineate and group areas based on their local climate and other characteristics. Knowledge is often shared well across a certain distance, but anthropogenic climate change has dramatically increased the spatial scale that regions must consider. This reality is true for all crops, with our approach and findings especially relevant for other perennial crops that cross regional to global scales [51,52]. Our approach here suggests how global analyses can complement regional studies, by providing insights into which regions are changing the fastest in response to anthropogenic warming to date versus which are growing grapes in the most extreme conditions. Such a global perspective lays the groundwork to compare with regional analyses and test if shifts are similar across scales or highlight important differences. As increased data becomes available such studies could provide major insights into questions critical to adapting crops to climate change, including how climate affects quality [e.g., [53]], and how extreme events versus mean trends impact yields and crop resilience.

**Supporting information**

**S1 Text**

(PDF)

Additional text detailing methods for aggregation and for using ERA5 data, tables showing all metrics with and without variety diversity for each country (for BEST/GPCC and ERA5), and supporting figures.

**Acknowledgments**

Thanks to J. Ngo for assisting with Fig 1 and two reviewers for their valuable comments.

**Author contributions**

**Conceptualization:** E. M. Wolkovich, Benjamin I. Cook, Iñaki García de Cortázar-Atauri, Thierry Lacombe, Ignacio Morales-Castilla.

**Data curation:** Cécile Marchal.

**Formal analysis:** E. M. Wolkovich, Benjamin I. Cook, Victor Van der Meersch, Ignacio Morales-Castilla.

**Visualization:** E. M. Wolkovich, Ignacio Morales-Castilla.

**Writing – original draft:** E. M. Wolkovich.

**Writing – review & editing:** E. M. Wolkovich, Benjamin I. Cook, Iñaki García de Cortázar-Atauri, Victor Van der Meersch, Thierry Lacombe, Ignacio Morales-Castilla.

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