# Skillful decadal prediction of unforced southern European summer temperature variations

Leonard Borchert, V Koul, Matthew B Menary, D Befort, Didier Swingedouw,

G Sgubin, Juliette Mignot

► To cite this version:

Leonard Borchert, V Koul, Matthew B Menary, D Befort, Didier Swingedouw, et al. Skillful decadal prediction of unforced southern European summer temperature variations. Environmental Research Letters, 2021, 16 (10), pp.104017. 10.1088/1748-9326/ac20f5. hal-03971666

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# Skillful decadal prediction of unforced southern European summer temperature variations

To cite this article: L F Borchert et al 2021 *Environ. Res. Lett.* **16** 104017

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LETTER

Skillful decadal prediction of unforced southern European summer temperature variations

L F Borchert<fnref n="1" />,<fnref n="2" />,* ID, V Koul<fnref n="3" />,<fnref n="4" />, M B Menary<fnref n="1" />,<fnref n="2" /> ID, D J Befort<fnref n="3" /> ID, D Swingedouw<fnref n="6" />, G Sgubin<fnref n="6" /> and J Mignot<fnref n="1" />

E-mail: leonard.borchert@lmd.ipsl.fr

Keywords: climate prediction, CMIP6, European summer temperature

Supplementary material for this article is available [online](#)

Abstract

We assess the capability of decadal prediction simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) archive to predict European summer temperature during the period 1970–2014. Using a multi-model ensemble average, we show that Southern European (SEU) summer temperatures are highly predictable for up to ten years in CMIP6. Much of this predictive skill, is related to the externally forced response: historical simulations explain about 90% of observed SEU summer temperature variance. Prediction skill for the unforced signal of SEU summer temperature is low: initialized model simulations explain less than 10% of observed variance after removing the externally forced response. An observed link between unforced SEU summer temperature and preceding spring Eastern North Atlantic—Mediterranean sea surface temperature (SST) motivates the application of a dynamical-statistical model to overcome the low summer temperature skill over Europe. This dynamical-statistical model uses dynamical spring SST predictions to predict European summer temperature, and significantly increases decadal prediction skill of unforced European summer temperature variations, showing significant prediction skill for unforced Southern European summer temperature 2–9 years ahead. As a result, dynamical-statistical models can benefit the decadal prediction of variables with initially limited skill beyond the forcing, such as summer temperature over Europe.

**1. Introduction**

Prediction of climate up to ten years into the future, so-called *decadal* climate prediction, has received considerable scientific attention due to its potentially high impact on society (Yeager and Robson 2017, Solaraju-Murali et al 2019, Merryfield et al 2020, Meehl et al 2021). On this time scale, the influence of chaotic internal climate variability and the response to external forcing strongly overlap (Lehner et al 2020). While the response to external forcing from greenhouse gas concentration is slowly varying and therefore well-predictable, internal variability poses a challenge to predictions (Doblas-Reyes et al 2013,

Marotzke et al 2016). However, due to its relatively slow characteristic time scales, internal variations of the ocean can be predicted in some places like the North Atlantic (Yeager and Robson 2017, Borchert et al 2021). Such predictable internal ocean variations could yield skillful prediction of unforced European summer temperature. Here, we examine whether ocean variations influence the skill of decadal-scale summer temperature predictions over Europe and, if so, whether this influence can be invoked to improve such predictions.

Most previous attempts to predict European tem-
perature analyzed models from the 5th phase of the
Coupled Model Intercomparison Project (CMIP).

<footnote n="1">¹ Sorbonne Universités (SU/CNRS/IRD/MNHN), IPSL Laboratory, Institut Pierre Simon Laplace (IPSL), Paris, France</footnote>

<footnote n="2">² Laboratoire de Météorologie Dynamique (LMD), École Normale Supérieure (ENS), Paris, France</footnote>

<footnote n="3">³ Institute of Coastal Systems Analysis and Modeling, Helmholtz-Zentrum Hereon, Geesthacht, Germany</footnote>

<footnote n="4">⁴ Institute for Oceanography, CEN, Universität Hamburg, Hamburg, Germany</footnote>

<footnote n="5">⁵ Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, United Kingdom</footnote>

<footnote n="6">⁶ EPOC, Université de Bordeaux, Pessac, France</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
Meanwhile, the decadal prediction simulations from the CMIP6 archive's Decadal Climate Prediction Project (DCPP) (Boer et al 2016) have been shown to have improved in predicting North Atlantic SST (Borchert et al 2021), both due to an improved response to volcanic forcing and improved capability to simulate internal climate variations. DCPP simulations have also contributed to studies showing accurate reproduction of observed extremely warm summer frequencies over Europe (Borchert et al 2019), winter North Atlantic Oscillation (Smith et al 2020), jet stream position (Ruggieri et al 2021), and atmospheric blocking (Athanasiadis et al 2020). These findings inspire hope that CMIP6 decadal prediction simulations also show skillful prediction of European summer temperature variability.

However, previous studies generally found low-
to-insignificant decadal temperature prediction skill
over Europe beyond the forced response (Hanlon et al
2013, Liu et al 2019, Wu et al 2019). At the same time,
some studies did demonstrate skillful temperature
predictions beyond the linear trend in parts of Europe
(Müller et al 2012, Arthun et al 2017, Feldmann et al
2019, Sgubin et al 2021). European summer tem-
perature was shown to be relatively well-predictable
compared to other seasons (Müller et al 2012), not-
withstanding relatively low skill beyond the linear
trend for this metric (Wu et al 2019). Any skill over
Europe is commonly attributed to variability of cli-
matic modes in the climate system (Doblas-Reyes et al
2013), many of which are in turn related to the relat-
ively slowly varying ocean (Yeager and Robson 2017).
As a result, numerous studies demonstrated skillful
decadal predictions of ocean temperature (Robson
et al 2012, Yeager et al 2012, Mignot et al 2016),
which is commonly highest in the North Atlantic
region (Matei et al 2012, Reintges et al 2020), due
to ocean circulation (Robson et al 2012, Yeager et al
2012, Swingedouw et al 2013). However, models have
been shown to be deficient in simulating observed
atmospheric teleconnection mechanisms that trans-
port that information from oceans to land (Qasmi
et al 2017, Borchert et al 2018), which limit predic-
tion skill over Europe. Any accounts of skillful predic-
tion over Europe are often limited to relatively small
regions and/or time scales, and draw on the memory
of the ocean (Arthun et al 2017).

A few studies presented evidence that coupling a
statistical model to dynamical and skillful SST predic-
tions can yield skillful decadal prediction of European
summer air temperature based on average North
Atlantic SST (Wu et al 2019), European late summer
precipitation (Simpson et al 2019), and the Indian
summer monsoon (Sahastrabuddhe and Ghosh
2021). Such hybrid dynamical-statistical (hence-
forth dyn-stat) predictions rely on observed links
between SST (the predictor) and European climate
(the predictand) to translate predicted SST into pre-
dictions of continental climate, thus circumventing

the deficiencies of climate prediction models con-
cerning potential teleconnections. Crucially, such
models require that predictions of the predictor
are skillful, and that the connection between pre-
dictor and predictand is robust (Simpson et al
2018).

Here, we provide a first stocktake of the capab-
ility of CMIP6 decadal prediction systems to predict
European summer temperature. We also develop and
apply a simple dynamical-statistical model based on
North Atlantic SST prediction to investigate its poten-
tial to predict the unforced component of European
summer climate. By comparing dyn-stat model and
the decadal predictions, we examine the added value
of a dyn-stat model for predicting unforced mod-
ulations of European summer temperature using
CMIP6, which identifies the potential that exists for
decadal temperature predictions over Europe in these
simulations.

## 2. Models and methods

2.1. CMIP6 models

Our analysis is based on an ensemble of eight ini-
tialized model systems, with ten ensemble members
each, from the DCPP (table S1 available online at
stacks.iop.org/ERL/16/104017/mmedia). The simu-
lations provided using these systems are called re-
forecast or hindcasts (HC). DCPP simulations are
initialized with observations every year after 1960
and run out for ten years (or lead years). We here
analyze the period 1970–2015, which is the longest
possible period that contains simulations from all
ten lead years, since the last starting year for some
models is 2014. For example, year 1970 is repres-
ented in lead year 10 of hindcasts started in 1960,
lead year 9 of the 1961 hindcast, lead year 8 of the
1962 hindcast, and so on. Additionally, ensembles of
historical simulations (HIST) with 28 CMIP6 mod-
els are analyzed (see table S1 for details). In total,
we analyze a large multi-model ensemble (MME)
of 192 historical members, subject to common for-
cing with observed atmospheric greenhouse gas con-
centrations, anthropogenic aerosols, solar variations
and volcanic eruptions (Eyring et al 2016). These
historical simulations are otherwise run freely to
simulate diverse internal climate variability around
the common forced response. Forming an ensemble
mean across these members thus approximates cli-
mate variations that arise in response to external for-
cing (Maher et al 2020). In contrast, the 80 hindcast
members have external forcing as well as the initial-
ised observed climatic state at the start of their integ-
ration in common. This improves the ability of these
systems to predict decadal internal climate variabil-
ity (Keenlyside et al 2008, Doblas-Reyes et al 2013,
Marotzke et al 2016). Forming an ensemble mean
across these members thus aims at approximating the
full climate evolution of a given period.
All model output is remapped to a latitude-
longitude 1 × 1° grid prior to analysis. We analyze
the MME mean as the mean of the single-model
ensemble means (i.e. a one-model-one-vote hier-
archy). In decadal hindcasts, we analyze averages
across lead year ranges 2–5, 6–9 and 2–9. We subtract
the individual ensemble member mean for each lead
year individually over the period 1970–2014 from
the respective member prior to all analysis. As this
is done for individual lead years, equating to a lead-
time dependent mean bias correction (Boer et al
2016).

## 2.2. Observations

For observational reference, we consider the grid-
ded observational temperature data sets from the
Hadley Center, namely HadCRUTv5 (Morice et al
2021) for surface air temperature (SAT), and interpol-
ated HadISST (Rayner et al 2003) for SST. Sea level
pressure (SLP) is obtained from the ERA5 reanalysis
(Hersbach et al 2020). Our results are robust to the
use of ERA5 instead of HadCRU/ISST for temper-
ature over both Europe and the North Atlantic (not
shown). In all cases, we remap the information to
a regular 1 × 1° grid, and analyze the period 1970–
2015.

## 2.3. Methods

We analyze spring (March–April–May, hereafter MAM) mean SST as well as summer (June–July–August, hereafter JJA) mean SAT. We evaluate the skill of HC and HIST against observations using the anomaly correlation coefficient (ACC), and the explained observed variance for different types of model simulations as ACC² (Goddard et al 2013). Significance is tested at the 5% level using a Monte Carlo bootstrapping method, where the underlying data is resampled 1000 times with replacement (Smith et al 2020). In addition to the bootstrapping, we benchmark the predictions against persistence forecast, which is calculated as the correlation of the observational time series with itself at different time lags. Finally, we evaluate the sensitivity of residual skill estimates to the exclusion of individual years in the analysis, using a leave-one-out cross validation analysis.

We test two different approaches of extracting
the forced response. As a first approximation of the
forcing trend, we extract the long-term linear trend
from simulations and observations. This technique
has, however, been shown to potentially introduce
spurious variability (Trenberth and Shea 2006, Mann
et al 2014, Tandon and Kushner 2015). We also extract
the forced component using the residuals approach
(Smith et al 2019). This technique uses the histor-
ical MME mean to estimate the forced component,
which is linearly regressed on HC or observed climate
variability, and then subtracted from each. We use
residuals to assess unforced (i.e. internally generated)

decadal prediction skill as well as study unforced
physical interactions in the climate system.

## 3. Decadal predictions of European summer temperature in CMIP6

Decadal prediction skill in terms of ACC for summer temperature throughout Europe is highly significant at lead years 2–9 in the HC MME (figure 1(a)). Skill inferred from HIST simulations that represent the forced response is similarly high (figure 1(b)), indicating that much of the signal in decadal European summer temperature variations that can be predicted in initialized prediction systems arises from externally forced variations. The high skill in HIST simulations is also found when using the same eight models as used in HC instead of the full 28 model ensemble (not shown). We use the full ensemble in this study for robustness.

An attempt to characterize unforced skill is shown in figures 1(c) and (d), using linear detrending. This approach, however, appears ill-fit to extract the forced response: as HIST approximates the forced response of the system, skill should be close to 0 after linear detrending if linear detrending was appropriate to extract the forced response (Branstator and Teng 2012, Gastineau and Frankignoul 2015, Wu et al 2019). Skill of HC and HIST after linearly detrending is non-zero and significant in large parts of Europe, particularly in central-to-eastern as well as northern and southeastern Europe. A more elaborate technique thus needs to be used when aiming to assess unforced variability and predictability of climate (Tandon and Kushner 2015).

One such approach of estimating the unforced variability based on historical simulations is the residual (Smith et al 2019, see Methods). Applying this methodology on a grid point basis in our skill calculation (figure 1(e)) shows that there is little summer temperature ACC skill in the HC simulations beyond the forced response: residual ACC is insignificant across much of Europe, with a small band of significant residual ACC across western Iberia, and a tendency of higher residual ACC towards southern Europe.

## 4. Using decadal predictions of seasonal SST to predict unforced European summer temperature

Given the overall low skill for unforced summer tem-
perature variability over Europe (cf figure 1), we now
develop a simple dynamical-statistical model to over-
come this issue. Here, a connection between SST and
SAT over land is identified, explored and exploited
in such a way where skillful dynamical decadal pre-
dictions of residual SST are invoked to predict the
residual of JJA SAT. Because of the—modestly—high
residual ACC for summer temperature in southern
<figure><img src="image_9.png" /></figure>

Europe (cf figure 1(e)), we construct a dyn-stat model aimed at predictions of SEU (10° W–30° E, 35°–50° N; black outlines) summer temperature over land, masking out ocean regions.

## 4.1. Observed SST impact on European summer temperature

To avoid overlap between the training and validation period of our dyn-stat model, we here explore the impact of unforced spring SST variations on summer SAT over southern Europe for the period 1900–1969, and MAM SST prediction skill during 1970–2014 (figure 2). Unforced (i.e. residual) spring SST shows high correlation to SEU summer SAT in the subpolar and eastern North Atlantic, as well as the Mediterranean in the observations (figure 2(a)). Much of this pattern is significant. The season lag between the two signals suggests that spring SST influences summer SAT.

The predictor needs to be predictable to be a useful predictor for SEU summer temperature in the dyn-stat framework. Significant residual skill for spring SST in the HC ensemble during 1970–2014 (figure 2(b)) is confined to the northeast subpolar as well as the eastern North Atlantic and the

Mediterranean Sea. The eastern part of the North Atlantic off the coast of Portugal and the western Mediterranean (the ENAMED region 25° W–15° E, 35°–45° N; blue lines in figures 2(a) and (b)) appears particularly suitable to construct the dyn-stat model, where unforced spring SST is both connected to summer SEU SAT and significantly predictable.

Spring SST in the ENAMED region is correlated with summer SAT over much of southern Europe, confirming the selected SST region to have an impact there (figure 2(c)). Spring-summer sea level pressure patterns suggest that this connection is accomplished by a combination of dynamic transport of atmospheric heat of oceanic origin around a high pressure system centered over the Mediterranean (figures 2(d) and (e)) and the persistence of heat in the Mediterranean region itself.

Findings presented here are robust to the use of different seasonal lead times between SST and summer SAT (e.g. using SST in preceding winter or simultaneous summer; not shown), illustrating the broad influence of ENAMED SST on SEU summer SAT. Heat persistence in the ocean is therefore the likely carrier of the spring SST signal to summer. Spring does, however, show larger residual SST prediction
<figure><img src="image_10.png" /></figure>

skill compared to the other seasons (summer SST is particularly unpredictable; not shown), which is why we here concentrate on spring SST as predictor of European summer temperature.

The local SST and SAT correlation patterns shown for the period 1900–1969 in figures 2(a) and (c) are also found during the period 1970–2014 (figure S1). A tripole structure observed in the recent period in the residual spring SST correlation pattern to SEU summer SAT, characterized by negative correlations in the subpolar and tropical North Atlantic and a band of positive correlations in between, inhibits the use of the high subpolar ACC for spring SST (figure 2(b)). Nonetheless, these findings highlight that the ENAMED-based statistical model trained on 1900–1969 is also applicable during the 1970–2014 period.

The correlation of the ENAMED-MAM and the SEU-JJA indices is also exemplified in their time series (figure 3). During the period 1900–1969, the forced response is characterised by a cold-warm-cold pattern (figure 3(a)). Both residuals show pronounced multidecadal variation, ending at a warm state at the end of the 1960s (figure 3(b)). After 1970, the forced response causes both indices to rise over time, and both residuals show a cold-warm-cold pattern in time (figure 3(b)). Summer SEU SAT thus follows the low-frequency behavior of spring ENAMED SST reasonably well and robustly throughout time. As the residual potentially explains about half of the forced response—standard deviations for observed JJA SEU SAT are: full signal 0.52; forced component 0.47; residual 0.23—predicting both forced response and unforced variability accurately is important to
<figure><img src="image_11.png" /><figcaption>**Figure 3.** Time series of HadISST/HadCRU observed (a) full and (b) residual observed surface temperature in SEU during summer (JJA) and the Eastern North Atlantic (ENAMED) during spring (MAM). In (a), solid lines show the full signal, whereas dashed lines show the forced component (FC), estimated from the historical multi-model mean.</figcaption></figure>

achieve decadal prediction of the full summer temperature signal in southern Europe.

## 4.2. Dynamical-statistical predictions of European summer temperature

The dyn-stat model developed here is inspired by more complex models (Simpson et al 2019, Wu et al 2019). The underlying idea is that potential deficiencies in the atmospheric component of climate models that hamper prediction skill over land (Meehl et al 2021) can be overcome with statistical models, after a physical link between a predictor and a predictand has been identified. Our dynamical-statistical model of JJA SEU SAT rescales the predicted MAM ENAMED SST with the ratio of standard deviations between the two indices observed during 1900–1969. The dyn-stat model thus requires skillful prediction of MAM SST and as such highlights the importance of initialization to achieve skillful decadal climate prediction.

There is reasonable area overlap between the area of observed positive correlation between ENAMED SST and SEU SAT (figure 2(c)) and skill in the dyn-stat model (figure 4(a)), indicating that the skill of the dyn-stat predictions arises from the observed link between ENAMED SST and SEU SAT. Dyn-stat residual ACC is significant over most of the SEU region south of 45° N, extending from the Iberian Peninsula and southern France to the coasts of the Black Sea (figure 4(a)). However, significant skill improvements compared to dynamical predictions are significant only over Italy and part of the Balkans, as well as

the Mediterranean itself (figure 4(b)). Beyond these regions, we find positive but generally insignificant improvement of residual ACC for European summer temperature in the dyn-stat model compared to the fully dynamical prediction.

The value of the dyn-stat model over purely dynamical predictions can further be illustrated in area mean skill for SEU summer temperature over land, explicitly masking out skill improvements over the Mediterranean Sea (figure 5). For the full SEU summer SAT signal, both dyn-stat and the HC ensemble are highly skillful across all examined lead time ranges, beating persistence forecast (figure 5(a)). The dynamical hindcast MMEs explain between 82% and 92% (dyn), and between 85% and 92% (dyn-stat), of observed SEU SAT variance, depending on lead time, the historical MMEs explain between 85% and 90%. This highlights the importance of the forced response for prediction skill, and is related to strong trends in both indices (figure 5(c)).

We find insignificant residual skill in both the dyn-stat and dynamical model for individual lead years (not shown) as well as four year lead time averages (figure 5(b)). Only at eight year lead time averages does significant residual ACC emerge in the dyn-stat model. Such relatively high skill at long temporal averages relates to filtering of noise through smoothing of the time series (Hegerl et al 2021). Dyn-stat skill at lead years 2–9 is also the instance where we find significant residual skill that is robust to a leave-one year-out cross validation. This analysis highlights the
<figure><img src="image_12.png" /><figcaption>**Figure 4.** (a) Residual ACC for summer (JJA) temperature, based on dynamical-statistical predictions using spring (MAM) ENAMED SST. (b) Difference of this dyn-stat residual ACC to the dynamical residual ACC shown in figure 1(e). Stippling indicates significance at the 5% level, based on 1000 bootstraps. Outlines indicate the SEU region.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 5.** ACC (y-axis) for (a) the full and (b) the residual summer (JJA) SEU temperature signal across lead time averages (x-axis). The dynamical model is shown in blue and the dyn-stat model based on spring (MAM) ENAMED SST predictions in cyan. Skill of the historical models (vertical line) and the historical MME mean (dots) is shown. The dashed line in (a) shows persistence skill as a benchmark, as do the short dashes in (b). Long dashes in (b) indicate the 5% significance threshold, estimated based on bootstrapping a dummy time series with similar characteristics as the observations 1000 times with replacement. Colored dashes in (b) denote maximum and minimum skill of a leave-one-out cross validation skill analysis, where each individual year was left out of the skill calculation once to highlight the importance of individual years for the diagnosed skill. (c) and (d) show the time series corresponding to lead years 2–9 in (a) and (b), respectively. The solid black line shows residual SEU observations, the dashed line in (d) shows the observed residual ENAMED index, and the dashed line in (c) indicates the forced component of SEU summer temperature based on the multi-model mean of the historical simulations.</figcaption></figure>

value of the dyn-stat model developed here as well as low-pass filtering to achieve robustly significant skill 2–9 years into the future.

The underlying time series of these analyses (figures 5(c) and (d)) illustrate the nuances that yield the skill improvement described above. On the full time series, the dyn-stat and dynamically predicted SEU summer temperature variations are very similar, explaining their similar full ACC (figure 5(c)). A comparable impression arises for the predicted residuals (figure 5(d)), due to the relationship between the two

indices in their unforced variability described above. The dyn-stat model reproduces the observed low-frequency cold-warm-cold variations of unforced SEU summer SAT better than the dynamical prediction does, explaining the elevated skill in dyn-stat. This confirms our hypothesis from the observed time series (cf figure 3) that the ENAMED SST index mainly captures low-frequency changes in unforced summer SEU SAT while the teleconnections are not well reproduced in the dynamical hindcasts.
## 5. Discussion and conclusions

Analyzing a MME of DCPP simulations, we have shown that these simulations show high skill for predicting European summer temperature. Much of this can be attributed to skillful historical simulations representing the forced response. As a result, DCPP simulations show low skill at predicting decadal unforced (residual) European summer temperature variations. In other words, the role of forcing for predictability of decadal variations of European summer temperature appears to be slightly larger than previously thought (Smith et al 2019). Based on observed teleconnections between spring ENAMED SST and summer SEU SAT, we present a dynamical-statistical approach to overcome the issue of limited residual skill over Europe. The dyn-stat model beats the HC ensemble as well as persistence at predicting decadal variations of SEU summer temperature.

In CMIP6, the skill of both initialised (HC) and non-initialised (HIST) decadal predictions of summer SAT are comparable, and improved compared to CMIP5 (e.g. Smith et al 2019). However, the strength of the external forcing appears to mitigate the potential for additional skill to arise from initialisation more strongly in CMIP6 than it used to be the case in CMIP5 (Smith et al 2019). This is probably related to improved model response to forcing in CMIP6 compared to CMIP5 (Borchert et al 2021). Based on our dyn-stat model results, limited residual summer temperature ACC may be caused by underestimated teleconnection between ENAMED SST and SEU SAT in models, either due to structural deficiencies in models or due to shock from the initialization procedure. The problem of underestimation of atmospheric signals is exemplified by the amount of summer SAT variance that is explained by spring SST: at lead years 2–9, the unforced ENAMED index explains 28% of variance of the SEU index (based on ACC²) in HC simulations, while the same calculation in observations yields a value 50%. This, as well as the low amplitude in predicted indices presented in this study, is likely related to the general underestimation of decadal climatic signals in climate models, known as the signal-to-noise problem (Smith et al 2020). As such, improving the response of atmospheric models to oceanic variability is crucial to improved skill in dynamical predictions at least to the level demonstrated by our dyn-stat model.

In this study we have used the residuals approach (Smith et al 2019) to remove the forced signal from observed and predicted time series. Recently, an alternative method was also presented (Sospedra-Alfonso and Boer 2020). Both of these methods utilize the historical MME to estimate the forced response and, for large enough ensembles, should yield similar mean-skill values. While the residuals (Smith et al 2019) are relatively

straight-forward to calculate and thus interpret, the alternative method (Sospedra-Alfonso and Boer 2020) provides additional estimates of unpredictable noise based on the ensemble spread. Here, given our focus on multi-model means, both methods should give similar results, so we use the more established residual method for its simplicity complementing it with additional bootstrap analyses to estimate uncertainties.

In previous studies, it was demonstrated that SST impact on European summer temperature was accomplished via a Rossby wave train (Borchert et al 2019, Wu et al 2019). This mechanism is partly different from what we describe here. In our case, ENAMED SST during spring and summer influences summer SEU SAT through a combination of advective transport of local heat anomalies and dynamic transport of heat from the North Atlantic around a high-pressure system over the Mediterranean. This difference in physical mechanisms might originate from either the consideration of the SEU region specifically, or from the use of residuals instead of the linearly detrended indices considered in the other studies. When using linear detrending instead of residuals, we found the Rossby wave train atmospheric response (not shown). As we show linear detrending to be deficient to extract the forced response, the Rossby wave train discussed in earlier work results at least partly from a response to forcing.

The timeseries of residual SEU summer temperature after 1970 (cf figure 3(b)) is reminiscent of the winter NAO low-frequency time series (Smith et al 2020, their figure 2), which might be related to the SST pattern that characterizes the Atlantic Multidecadal Variability (AMV) (Sutton and Hodson 2005, Klavans et al 2019). There is evidence that these two indices are not independent, and indeed that a AMV-like SST pattern is forced by fluctuations of the winter NAO (Klavans et al 2019, Oelsmann et al 2020), so there might be a potential role of the NAO in driving recent unforced summer temperature variations in the SEU region (Sutton and Hodson 2005). The ENAMED SST index representing the low-frequency part of SEU variations (cf figure 3(b)) fits this narrative. It is thus possible that NAO plays a role in modulating the ENAMED SST that is related to SEU summer temperature as described here, probably through an impact on SST that persists into later seasons.

The dyn-stat model used here is kept very simple for the sake of easy interpretability. There are more complex options, such as EOF-based models (Wu et al 2019) or multivariate regression models (Simpson et al 2019). Considering more complex processes or SST dipoles thus holds the potential to increase the skill found in the dyn-stat model here even further; the skill estimates shown in this study are a lower bound on expectable skill in that sense.
A general shortcoming of the residual approach is that it includes the impact of volcanic eruptions in the forced response, although eruptions would probably benefit the skill of initialized predictions in a forecast setting. The HIST simulations used in residual calculation include the effect of volcanic forcing, which would not be known in forecasts of the future. Volcanoes, however, appear to be particularly important for decadal climate predictions in the North Atlantic region (Hermanson et al 2020, Borchert et al 2021). For a more nuanced assessment of the value of initialization, only greenhouse gas and anthropogenic aerosol forcings—as used in climate projections—should therefore be used to calculate residuals in hindcast studies. We do not do this here because the current protocol for hindcast initialization is also unfit for a hindcast assessment fully consistent with forecasts, in that the information of volcanic eruptions is included in hindcast forcing terms at the exact time of the eruption and not (as would be the case for a forecast) at the next initialization time after the eruption.

Our work highlights the importance of external forcing for the capability of decadal prediction systems to predict European summer temperature. Invoking links to predictable unforced SST, however, the resulting limited skill for internal summer temperature variations can be overcome to some extent, emphasizing the importance of skillful initialization of SST prediction to predict climate over land. Our results thus provide hope for skillful predictions of European summer temperature variations in future modeling systems when the representation of teleconnections is improved.

## Data availability statement

The data that support the findings of this study are openly available at the following URL/DOI: https://esgf-node.ipsl.upmc.fr/.

## Acknowledgments

The authors acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. The authors thank the climate modeling groups (listed in table S1) for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. L F B, D J B, D S, G S and J M were supported by the EUCP project funded by the European Union's Horizon2020 programme, Grant Agreement Number 776613. J M and D S were also supported by the H2020 Blue-Action project, Grant Agreement Number 727852. M B M was supported by the H2020 EPICE project, Grant Agreement Number 789445. L F B and

M B M received additional support from the ANR-TREMPLIN ERC Project HARMONY, Grant Agreement Number ANR-20-ERC9-0001. L F B especially thanks Johanna Baehr for the warm welcome to her group in the difficult times of COVID-19. HadISST and HadCRU data were obtained from www.metoffice.gov.uk/hadobs and are © British Crown Copyright, Met Office, 2021, provided under a Non-Commercial Government Licence www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/.

The authors thank anonymous reviewers for their valuable help in improving the manuscript.

## ORCID iDs

L F Borchert https://orcid.org/0000-0002-3232-7409

M B Menary https://orcid.org/0000-0002-9627-2056

D J Befort https://orcid.org/0000-0002-2851-0470

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