The Chablis wine region is located in the department of Yonne, in the northern part of Burgundy, France (Figure 1). The vineyards are within a relatively compact area (approximately 16 km (North–South) by 18 km (East–West) centred around the town of Chablis (latitude 47°48′49″ N, longitude: 3°47′54″ E, 140 metres above sea level). The topography is hilly, rising to around 320 metres and the vineyards lie on both sides of the river Serein which runs broadly North–South through the town. Chablis wines are divided into four appellations d'origine contrôlée (AOC). In decreasing order of quality recognition, these are Grand Cru Chablis, Premier Cru Chablis, Chablis and Petit Chablis. For the purposes of this study, all four AOCs are included in the term “Chablis wine”.

Table

Vineyard areas in orange (European Environment Agency, 2020), study area weather stations (WS, lilac circles), and grid square (dashed square) used for climate projections (Drias, 2021).

Monthly weather data for Chablis were taken from the French meteorological service, Météo-France. The Chablis weather station (number 89068001) lies on the outskirts of the town of Chablis at latitude 47°49'19" N, longitude 3°47'26" E and elevation 141 m. It does not record sunshine duration, however. For this variable, the records from two weather stations in Auxerre, both approximately 19 km west of Chablis, were merged: Auxerre (latitude 47°48'05" N, longitude 3°32'43" E, elevation 207 m, from October 1962 to April 2013) and Auxerre-Perrigny (latitude 47°49'28" N, longitude 3°32'58" E, elevation 152 m, from April 2013 to October 2020).

This weather dataset comprised monthly readings from October 1962 (the earliest date available for key temperature measurements) to October 2020. The data were also used to generate climatic indices that are typically used for viticulture, including indices for growing season temperature and precipitation for the phenological phases important for wine quality. These included mean Growing Season Temperature (GST) (Jones et al., 2005), the Cool Night Index (CNI) which in the Northern Hemisphere is the mean minimum temperature for September (Tonietto and Carbonneau, 2004), and precipitation during veraison and/or ripening (Ashenfelter, 2010; Baciocco et al., 2014; Davis et al., 2019).

To enable comparison between the climates of Chablis and the Côte de Beaune, data from the Savigny-lès-Beaune weather station (number 21590001, latitude 47°03'13" N, longitude 4°50'07" E, elevation 246 m), approximately 113 km southeast of the Chablis weather station, for the period 1961 to 2020 were also collated.

Multiple linear regression was employed to develop a model of the impact of inter-annual variation in weather on the quality of Chablis wine (the “Chablis vintage model”). A range of regression approaches (manually based on exploratory Principal Component Analysis (PCA), best subset and forward stepwise) was used to create the model that explained the most variance in the vintage score (adjusted R-squared), but which also satisfied criteria for homoscedasticity (Breusch–Pagan test), the randomness of residual plots, normality of distribution (Shapiro–Wilk test) and leverage (Cook’s distance). A more complex model (i.e., one with more predictor variables) was accepted only if it passed the F-test, achieved a lower Bayesian Information Criterion BIC score, and led to a 2 or more unit improvement in Akaike Information Criterion (AIC) (Bevans, 2020).

Eight meteorological measurements were considered as candidate independent variables in the Chablis vintage model: mean temperature, mean maximum temperature, mean minimum temperature, mean daily temperature range, number of days equal to or exceeding 35 °C, total precipitation, number of precipitation days >1 mm, and sunshine duration. A total of 368 candidate model variables were calculated for all months singly, for periods ranging from two to nine months between February and October, and also the period November to March to cover the vines' dormancy period in the prior winter (Baciocco et al., 2014).

Despite April to October being the standard period for measuring GST for Northern Hemisphere viticulture (Baciocco et al., 2014; Moral et al., 2016), periods that ran through to October were subsequently eliminated as candidate model variables. This was because climate change has advanced the start of harvest in Chablis since 1980 by approximately 20 days from early October to mid-September (Biss, 2020); and so including data for October would have incorporated considerable data after grapes have been harvested. This concurs with research for other wine regions of France. Neethling et al. (2012) used the April to September period to represent GST for the Loire Valley, a similar latitude to Chablis (47 °N), as did Ashenfelter (2010) for Bordeaux.

Bootstrapping (10,000 resamples) was carried out with the R ‘boot’ package, using both case and residual resampling methods, to find 95 % confidence intervals (basic, percentile, and bias-corrected and accelerated (BCa)) for Chablis vintage model coefficients and adjusted R²;^{BCa is a methodology that corrects for bias and skewness in the bootstrap distribution.}

The vintage score increased significantly by around 2 points to 7.1 between 1963 and 2018 in Chablis (Table 1).

Several weather variables (sunshine and most temperature indices) but not all (indeed, none of the precipitation indices) also showed significant trends (Table 1). Mean spring/summer temperature(Tmean_Apr-Sep ) increased by almost 0.5 °C per decade between 1963 and 2020 and slightly more so than mean autumn/winter temperature cumulatively (Tmean_Apr-Sep 2.68 °C versusTmean_Oct-Mar 2.16 °C). The mean maximum rose considerably more than the mean minimum temperature (2.33 °C versus 1.03 °C for September). Similarly, the number of days reaching or exceeding 35 °C between 1st April and 30 September each year has risen from close to zero to almost six days; much of this occurred between 2015 and 2020 with 12, 5, 6, 7, 10 and 8 days in successive years, indicating a non-linear trend. Mirroring the above, the number of days where minimum air temperature fell below 0 °C between April and September more than halved.

Sunshine duration also increased, but this linear relationship was not as strong as for most temperature indices. No significant regression was detected for continentality, the difference in mean temperature between the warmest and coldest months (Skelton, 2007).

Table

titre du tableau
	Mean	SD	Gradient	SE	Total Trendline Change	Adj. R2	P
Vintage Score	7.12	1.60	0.037	0.012	2.05	0.13	0.004
Temperature Indices
TmeanApr-Sep	15.9 °C	1.03 °C	0.047 °C yr-1	0.005 °C	2.68 °C	0.59	<0.001
TmeanOct-Mar	6.2 °C	1.07 °C	0.038 °C yr-1	0.007 °C	2.16 °C	0.35	<0.001
TmaxApr-Sep	22.3 °C	1.43 °C	0.061 °C yr-1	0.008 °C	3.46 °C	0.50	<0.001
TminApr-Sep	9.4 °C	0.79 °C	0.033 °C yr-1	0.004 °C	1.89 °C	0.49	<0.001
TmaxSep	22.0 °C	2.06 °C	0.041 °C yr-1	0.015 °C	2.33 °C	0.10	0.010
TminSep (CNI)	9.4 °C	1.44 °C	0.018 °C yr-1	0.011 °C	1.03 °C	0.03	0.110
TrangeSep	12.6 °C	1.98 °C	0.023 °C yr-1	0.015 °C	1.3 °C	0.02	0.145
No. Hot Days Apr–Sep (>=35 °C)	2.5 d	3.43 d	0.105 d yr-1	0.023 d	5.97 d	0.25	<0.001
No. Frost Days Apr–Sep	6.7 d	3.58 d	–0.064 d yr-1	0.027 d	–3.66 d	0.08	0.021
Continentality	18.0 °C	2.36 °C	0.022 °C yr-1	0.018 °C	1.23 °C	0.01	0.247
Precipitation Indices
PApr-Sep	360.7 mm	83.72 mm	0.099 mm yr-1	0.662 mm	5.66 mm	0.00	0.881
PJun-Sep	234.0 mm	62.74 mm	–0.375 mm yr-1	0.494 mm	–21.36 mm	0.00	0.451
POct-Mar	364.4 mm	83.27 mm	0.412 mm yr-1	0.657 mm	23.5 mm	0.00	0.533
PAug	60.7 mm	35.33 mm	–0.273 mm yr-1	0.277 mm	–15.53 mm	0.00	0.330
PSep	58.9 mm	34.77 mm	–0.284 mm yr-1	0.273 mm	–16.19 mm	0.00	0.302
No. Rain Days Jun–Sep	34.0 d	7.38 d	–0.037 d yr-1	0.058 d	–2.11 d	0.00	0.528
No. Rain Days Aug	8.4 d	3.68 d	–0.037 d yr-1	0.029 d	–2.13 d	0.01	0.197
No. Rain Days Sep	8.4 d	4.07 d	–0.018 d yr-1	0.032 d	–1.05 d	0.00	0.567
Monthly daily max rain Apr–Sep	107.6 mm	25.49 mm	0.214 mm yr-1	0.200 mm	12.18 mm	0.00	0.289
Sunshine (Auxerre)
Sunshine Apr–Sep	75,038.7 min	7853.0 min	127.6 min yr-1	59.8 min	7274.9 min	0.06	0.037

Tmean = mean temperature (°C), Tmin = mean minimum temperature (°C), Tmax = mean maximum temperature (°C), TrangeSep = difference between the mean minimum and mean maximum temperatures for September (°C), Continentality = difference in mean temperature between the warmest and coldest months (°C) and P = total precipitation (mm), for stated multi-month periods (in subscript). Bold: significant trend (P < 0.05).

The regression line fitted over the study period (solid black line, Figure 2) explained over half of the variance in mean spring/summer temperature (Table 1). During this period, years have differed considerably in vintage rating for Chablis wine, but no Poor-quality vintages have been recorded since 1991 (Figure 2). Comparing temperature-year regressions amongst the three vintage classifications showed significant differences (P < 0.005): Excellent vintage rating years provided the shallowest slope, crossing the all-years temperature trend line in 1998. Hence overall, the post-2000 period provided a greater proportion of years with Good Chablis vintages than the 1963–1980 period (Figure 2).

Table

Also shown are the years, and the respective regression of mean temperature against these years only, in which the Chablis wine vintage was rated as Excellent (purple line, Adjusted R-squared 0.44, P = 0.003), Good (green line, Adjusted R-squared 0.51, P < 0.001), or Poor (red line, Adjusted R-squared 0.37, P = 0.04).

The best model identified and selected for the Chablis vintage model was:

(Equation 1) Vintage Score =

22.38 Tmean_Apr-Sep – 0.6790 Tmean_Apr-Sep ² – 0.4089 CNI – 0.006918 P_Jun-Sep – 170.9

where Tmean_Apr-Sep is mean temperature (°C) from 1st April to 30 September, CNI is the Cool Night Index (°C) and P_Jun-Sep is the total precipitation (mm) from 1st June to 30 September. The model and each term were significant and explained 57 % of the variance (Table 2) with a good fit to observations from Poor to Excellent scores (Figure 4).

Table

Chablis wine quality has improved during the study period, with no Poor vintages (score < 6) since 1991 (Figure 2), whilst summer temperature and sunshine duration have progressively increased with fewer frost days but no linear trend for precipitation (Table 1). This has gone hand-in-hand with a 73 % increase in vineyard area and, based on the 5-year moving average, an 86 % increase in wine production (BIVB, data by personal communication). The Chablis vintage score model (Equation 1) shows a clear historic link between recent climate change in the region, particularly warmer growing season temperature (GST; Figure 2), and better vintage scores (Figure 4). In addition to this benefit from climate change, Chablis vintage score is also likely to have risen through better vitivinicultural practices such as superior hygiene and frost protection since the 1960s (George, 2007). The effect of temperature on the vintage score quantified here (Table 2) will, in part, also have captured the improvement over time in vitivinicultural practices because of the warming over this period (Figure 2).

Providing GST is below the maximum of a cultivar's range (see below), warmer GST results in greater photosynthetic production of carbohydrates improving flowering and fruitset (Atkinson, 2011) with more reliable ripening (Jones et al., 2005). In addition, phenology is advanced so that veraison and ripening occur earlier in the year when temperatures are warmer (van Leeuwen and Darriet, 2016; Leolini et al., 2018). Earlier budburst does imply closer coincidence with more frost-prone months and so has the potential to reduce yield (Leolini et al., 2018), as occurred throughout Europe, and specifically France, in Spring 2021.

The quadratic relationship detected between Chablis vintage score and Tmean_{Apr-Sep (Figure 5), a pattern observed for wines in other regions (Jones et al., 2005), indicates that Chablis quality may not necessarily continue to improve with further warming. The peak was at 16.5 °C (Figure 5). The 2009–2018 mean value for TmeanApr-Sep (16.8 °C, Table 4) is at the warm end of the optimum plateau for the vintage score evident in Figure 5, and so further warming may reduce quality. This comment is reinforced by the different regressions of temperature with the year for each rating category where the combined and Good quality relations are almost identical whereas that for Excellent quality is shallower in slope (Figure 2); extrapolation of that relationship into the future, though unwise, would suggest a lower probability of achieving an Excellent vintage of Chablis typicity.}

The use of monthly weather data precluded the generation of certain temperature-based indices which accumulate above daily temperature thresholds (typically 10 °C in grape) such as Growing Degree Days (GDD), Biologically Effective Degree Days (BEDD), Huglin's Heliothermal Index (HI), or the Winkler Index (WI, based on GDD). This is, however, not considered an issue; mean Growing Season Temperature (GST) as used here and advocated by Jones et al. (2005) and widely used is highly correlated with the other indices for measuring growing season warmth (Moral et al., 2016) and is functionally no different to GDD (Anderson et al., 2012).

In our multi-factor model, temperature (Tmean_Apr-Sep ) was the best weather factor explaining inter-annual variation in Chablis vintage score. This is consistent with studies on Chardonnay in Burgundy (Outreville, 2018; Davis et al., 2019), in other French regions such as Bordeaux (Baciocco et al., 2014; Ashenfelter, 2017), Rhone (Ashenfelter, 2017) and the Loire Valley (Neethling et al., 2012) with different cultivars, and regions outside of France such as Barolo in Italy and Barossa in Australia (Ashenfelter, 2017).

It is well established that sugar concentration in grape berries is positively correlated with GST (van Leeuwen and Darriet, 2016); as is the case for Chardonnay (Gambetta et al., 2016). Sugar concentration determines the potential alcohol content of the wine and whilst it is a prerequisite for high quality, excess heat can ultimately lead to over-alcoholic wines which, if accompanied by low concentration of organic acids, are “unbalanced” (Jones et al., 2005; Neethling et al., 2012; van Leeuwen and Destrac-Irvine, 2017). The reasons for greater sugar content at warmer temperatures may involve water loss from berries due to evaporation and greater concentration of the sugar (Pastore et al., 2017) and/or physiological changes that may be genetically controlled and therefore cultivar specific (Suter et al., 2021). Moreover, the composition of secondary metabolites that are responsible for the organoleptic properties of wine, and so affect quality, is changed in grapes ripened at high temperature (van Leeuwen and Destrac-Irvine, 2017).

Mean temperature between May and July is particularly important for the subsequent quality of Chablis wine (Figure 3, Table 2). Flowering and fruitset typically occur in the first half of June for Burgundy as a whole (Davis et al., 2019), though in Bordeaux, for example, can vary by around one month (Jones and Davis, 2000).

The polynomial relation for May to July mean temperature provided the best single-factor model for the vintage score (Table 2). A threshold of 15.5 °C for this period may provide a simple, easily-applied rule of thumb in advance of harvest to predict vintage quality; below this value vintage quality is likely to be Poor (83 % probability, Figure 3f). Warmth in this period is important for wine quality in other regions also (Real et al., 2017). If it is too cool the flowering period is prolonged and can lead to uneven berry ripeness and wines with vegetal characteristics (Atkinson, 2011), or variable berry size within a bunch (“Millerandage”) and/or berries that are incompletely fertilized (“Shot” berries) which hinder the production of a balanced wine (Gray and Coombe, 2009). Conversely, too high a temperature during flowering can cause premature veraison, inactivation of enzymes and incomplete biosynthesis of compounds associated with flavour (Jones et al., 2005).

The Cool Night Index (CNI) was the second most important weather variable, after GST (Tmean_Apr-Sep ), in the Chablis vintage model (Table 2). This term in the model accounts for the effect of cool temperature during the ripening period in the 30 days until harvest (Tonietto and Carbonneau, 2004). Whilst warmth in the day is crucial for berry ripening, cool temperatures during the night result in the secondary metabolites associated with high-quality flavours and aromas (Tonietto and Carbonneau, 2004). Moreover, recent research by Aoki et al. (2021), albeit on an indigenous Japanese grapevine variety (V. vinifera cv. Koshu), suggests high night temperatures may promote downy mildew. This may also be relevant because Chardonnay vines are susceptible to downy mildew infection which can taint the wine (Skelton, 2020).

High night temperatures also increase respiration and the degradation of malic acid (Arrizabalaga-Arriazu et al., 2020); and so cool nights preserve acidity in the berries. Acidity is an important aspect of Chablis typicity. Malic acid (usually converted to the smoother-tasting lactic acid by winemakers via malolactic fermentation) typically provides half of the total acidity of grapes and wine with tartaric acid (the other major acid) less prone to degradation during ripening (Jackson, 2014). Wines that lack malic acid (or lactic acid after conversion) may taste flat and are prone to microbial spoilage, although in excess wines can taste sour (Jackson, 2014).

It has been suggested that it is the difference in daily temperature range during ripening, rather than minimum temperature, produces important flavour and aroma compounds (Gladstones, 1992 cited by Jones et al., 2005). However, in developing the model we found that CNI (i.e., mean minimum temperature in September) explained more variance in the vintage score and also the effect of the range between mean minimum and mean maximum temperatures for September was not significant (P > 0.10). Chablis vintage quality is more closely associated with acidity than secondary metabolites, and so the effect on night respiration rates may explain why mean minimum temperature, not the range, in September is an important factor for Chablis.

Every 100 mm increase in P_Jun-Sep reduced the vintage score by almost 0.7 in the model (Table 2). Moderate water stress is also associated with higher quality wine (van Leeuwen et al., 2009; Fraga et al., 2013; Alem et al., 2019). Water stress increases the biosynthesis of secondary metabolites important to the development of aroma in wine, such as the carotenoid-derived C₁₃-norisoprenoids which are associated with floral and fruity odours (Alem et al., 2019). Too much rain between flowering and ripening is also well known to reduce wine quality (Jones and Davis, 2000). This is because berries swell producing lower quality wine (Jones and Davis, 2000; Baciocco et al., 2014) due to a reduction in the concentration of flavour- and aroma-related metabolites (VanderWeide et al., 2021). In cooler climates, high rainfall may lead to sour rot infection, which increases the concentration of acetic acid in berries imparting an unwanted vinegar flavour to the wine (VanderWeide et al., 2021). In this study, Poor vintages of Chablis with high P_Jun-Sep (approximately > 275 mm) were associated with cool Tmean_Apr-Sep , suggesting sour rot may have been an issue in these vintages.

We also compared a model with August to September precipitation (P_Aug-Sep ) rather than June to September (P_Jun-Sep ). The shorter period was assessed because research for other wine regions emphasised the importance of rainfall in the few weeks leading to harvest (Ashenfelter and Byron, 1995; Davis et al., 2019). In our assessment, however, the use of (P_Aug-Sep ) resulted in a non-significant (P = 0.0642) rainfall term. This comparison suggests that the Chablis vintage score is reduced more consistently by greater rainfall over a longer period than just the last weeks before harvest.

Several models of the influence of inter-annual variation in weather on wine quality in France have been devised since Ashenfelter, Ashmore and Lalonde (1993, cited by Ashenfelter and Byron, 1995) showed that much of the variability in the price of a Bordeaux vintage could be explained by its age, GST (growing season temperature, April to September), and rainfall both in August and September and also in the prior autumn and winter. Models which represent both the positive effect of temperature during the growing season and the negative effect of rainfall typically account for 35 to 60 % of the variance in the vintage score (Outreville, 2018) or can accurately categorise the top and bottom vintage scores (Baciocco et al., 2014; Davis et al., 2019). The Chablis vintage score model presented here (Table 2) is consistent with the above.

Focusing on Burgundy, Outreville (2018) found that temperature in July and August provided the best (positive) association and rainfall in August and September the best (negative) association with white wine quality. Davis et al. (2019) found that the impact of rainfall on white Burgundy wine quality varied with phenology, but the Chablis vintage model developed here, with monthly data, detected only the negative relation for June to September rainfall reported in Table 2.

The main difference between our Chablis vintage model and one for white Burgundy (Davis et al., 2019) is the inclusion of CNI, which we found to be a more important factor than rainfall.

This difference may be because acidity is more important for Chablis than other white wines from Burgundy (George, 2007). This would tally with the observation that while GST is around 1 °C lower in Chablis compared to Savigny-lès-Beaune, CNI is proportionally far cooler during the ripening period (Table 5) – important to the acid content of the berries. Moreover, minerality, another characteristic of Chablis wine, may be positively correlated with acidity (Ballester et al., 2013). Hence, CNI could be a key variable in the difference in typicities between Chablis and the white wines of the Côte de Beaune, and elsewhere.

Table

titre du tableau
Climate Indices	Chablis	Savigny-lès-Beaune
1963–2018
GST (TmeanApr-Sep ) (°C)	15.8	16.75
CNI (°C)	9.40	11.24
PJun-Sep (mm)	236.6	244.6
2009–2018
GST (TmeanApr-Sep ) (°C)	16.8	17.6
CNI (°C)	9.8	11.8
PJun-Sep (mm)	236.0	268.8

The mean minimum temperature in September (CNI) was an inappropriate factor for the 2003 vintage because the harvest started on 25 August (George, 2007). This was also partly the case for the 2007, 2011, 2015, 2017 and 2018 vintages for which harvest began in early September (Biss, 2020). In both cases, since harvest typically takes around two weeks to complete, August or mid-August to mid-September might be a more relevant period to consider for the CNI in future developments of the model, as suggested by the slight improvement in model performance achieved by using August and September instead of just September. Omazić et al. (2020) found the same problem with CNI for wine regions in Croatia due to earlier harvests.

A similar issue arises for GST and precipitation from flowering to harvest, with climate change advancing the beginning and endpoints for these indices. However, no model improvement was observed by advancing these weather factor periods by one month for the most recent 25, 20, 15 or 10 vintages. Indeed, the advancement to phenology versions of the model performed poorly, but this may have been because there were no Poor-quality wines in these recent periods (Figure 2) and so a narrower range of vintage scores available for analysis. A larger model incorporating the effects of weather on the timing of crop development, soil water balance (such as the Dryness Index (Tonietto and Carbonneau, 2004)), or one with greater temporal resolution and more precise data for wine quality, might be an improvement. All models are simplifications of the real world, however, and our current model has the virtues of simplicity and ease of application.

We have developed, and validated, a simple model here to describe the historic effect of inter-annual variation in weather on Chablis vintage score quality. Bonada and Sadras (2015) point out that such approaches are “bound to be inconclusive” because numerous factors are either confounded or analysed insufficiently. Such factors include wind, previous years' weather and vine development, water deficit, vapour pressure deficit, viticultural and oenological practices, and extreme weather events. Neither has the increase in atmospheric CO₂ concentration since 1963 been accounted for. This may affect wine quality with, for example, faster berry development leading to reduced malic and tartaric acid concentrations (Leibar et al., 2017; Arrizabalaga-Arriazu et al., 2020).

There is also the subjective basis of vintage scores (Hodgson, 2008; Cicchetti and Cicchetti, 2013; Jackson, 2014). Other issues include variation in phenology from year to year and the limitation of weather data from only one station given that inter- and intra-vineyard variation in temperature and bioclimatic indices can be as great as at larger scales (Bonnefoy et al., 2013). The above may help to explain why the Chablis vintage model accounted for only 57 % of the variance in the vintage score. We also acknowledge that the model was far better in distinguishing between the Poor and the two better vintages than between the Good and the Excellent (Figures 4 and 6). Nonetheless, the model provides a first approximation of the weather typically associated with high-quality Chablis wine production, compares well with similar models for other regions and/or cultivars, and, we argue, has utility in the comparative ease of application to different scenarios.

Poor Chablis vintages appear to have been avoided in recent years due to warming (Figure 2). Looking ahead, the median probability of the intermediate RCP 4.5 scenario in combination with the Chablis vintage model suggests that Chablis producers will struggle to maintain the high quality of their wine by 2041 to 2070 (predicted score 6.3, Table 4). However, if minimum temperatures in September (CNI) continue to rise more slowly than maximum temperatures the decline may be reduced (CNI2 predicted score 6.8, Table 4). On the other hand, warming is likely to hasten ripening (van Leeuwen and Destrac-Irvine, 2017) further into August which would further increase the effective CNI. Representative Concentration Pathway 2.6 is the only scenario that has a chance (> 66 %) of meeting the Paris Agreement (OECD, 2017); we predict that achieving that target would provide Chablis vintage score only marginally below that of recent years (7.4–7.7 versus 7.8 for 2009–2018).

Vintage scores assess whether wines are of typicity. A Good rating, for example, indicates the wine is not an Excellent example of a Chablis but it may well be very pleasurable to drink (Martin, 2020). Similarly, vintages with the same score may not be identical in all regards. The 2003 and 2018 vintages were noteworthy for being unusually hot (Tmean_Apr-Sep > 18 °C) and dry (P_Jun-Sep < 135 mm). Their ratings were Good (score 6.1) and Excellent (score 8.1), respectively, but they were not like typical Chablis wines lacking acidity (Robinson, 2019) and minerality (Martin, 2020). This consideration is relevant to future vintages. Tables 4 and 5 show that the Chablis region’s climate in 2041 to 2070, especially with the RCP 4.5 emissions scenario, may approach that of today’s Côte de Beaune, a region world-renowned for its premium quality Chardonnay wines such as Corton-Charlemagne, Chassagne-Montrachet, Meursault and Puligny-Montrachet. Hence, although climate change may reduce Chablis vintage score in future (Table 4), Chardonnay grapes grown in the Chablis region are likely to continue to produce premium quality wines.

These predictions must be approached with caution. In addition to errors in the Chablis vintage model, there are uncertainties associated with emissions scenarios and climate modelling (Jacob et al., 2014; OECD, 2017), including the possibility of decadal-scale cold waves (Sgubin et al., 2019). It is also the case that short-term extreme weather events, such as hail and intense rain – the frequency and strength of which will increase with climate change (van Leeuwen and Darriet, 2016) – are not accounted for in the above.

Moreover, it is expected that wine producers will adapt to and/or mitigate the effects of climate change through crop management (van Leeuwen et al., 2019; Santos et al., 2020). The Chablis vintage model presented here may support producers in that task. A further aim is for the model to be applied to identify sites with suitable climates for premium white wine from Chardonnay grapevines in emerging cool climate viticulture regions, such as the UK, an approach to site selection advocated by Ashenfelter (2017).