The renewable energy sector has gained increasing importance in Europe in recent years, with wind and solar representing a growing fraction of total electricity generation . Continued policy support and decarbonisation targets are expected to further accelerate this expansion in the coming decades . Renewable energy production depends strongly on variations in key climate variables such as near-surface air temperature, solar radiation or surface wind speed , and ambitious deployment targets across Europe further highlight the need for climate-informed planning in the energy sector (e.g., ; ). While long-term climate projections are essential for strategic planning, and short-term forecasts support daily operations, the intermediate horizon of the next few years remains comparatively underexploited . Decadal climate predictions, which bridge the gap between seasonal forecasts and long-term projections, are emerging as a valuable tool for sectors that must adapt to both climate variability and climate change . In the energy sector, such information could support infrastructure development, system resilience, and risk management decisions . Despite this potential, the explicit integration of decadal climate information into wind and solar energy assessments remains limited, highlighting a gap between advances in decadal prediction science and their application in the renewable energy sector.

Previous studies have evaluated the forecast skill of decadal prediction systems for essential climate variables and extreme indices , and have also explored the role of statistical downscaling in improving regional skill . In parallel, decadal forecasts have been applied to sector-specific climate services, including agriculture , water management , marine fisheries , and hydropower , as well as multi-sector studies demonstrating the application of decadal predictions . However, research on decadal climate services explicitly focused on the energy sector remains limited. Early assessments have reported regionally dependent skill for decadal predictions of wind-related variables, including near-surface wind speed and wind energy output, with skill often enhanced through downscaling approaches . More recent work has highlighted relatively high skill for surface solar radiation, while wind speed skill remains generally low over Europe . By comparison, climate services for the energy sector have been more extensively explored at sub-seasonal to seasonal timescales and with long-term climate projections .

In this study, we assess the skill of decadal predictions for energy-relevant indicators derived separately for solar and wind energy over forecast years 1–3, and we further explore compound indicators that combine information from both sources. This 3-year forecast period is particularly relevant for short- to medium-term energy planning applications, including maintenance scheduling, estimation of expected renewable energy production, and management of seasonal energy mixes. Both annual and seasonal indicators are considered: annual means highlight low-frequency signals relevant for year-ahead planning, while seasonal means allow the identification of season-dependent predictability. Analyzing each energy source independently allows us to characterize source-specific predictability, while the combined indicators capture the co-variability of wind and solar resources and its implications for energy system operations. Compound events are particularly relevant because simultaneous deficits in multiple energy resources can exacerbate the risk of energy shortages and stress the energy system . Previous studies have shown that combining wind and solar resources can enhance system robustness through their temporal and spatial complementarity . Decadal predictions of compound hot-dry extreme events have recently been explored by . However, to the best of our knowledge, an assessment of the forecast quality of decadal predictions for compound power indicators remains lacking, limiting the understanding of their utility for energy system planning and risk management over multi-year timescales.

This study is organized as follows. Section 2 describes the datasets used to compute the indicators including observation-based reference datasets, decadal predictions and historical simulations. Section 3 presents the methodology to compute the solar- and wind-based indicators, and compound indicators. Section 4 presents the results together with the discussion and a comparison with previous studies. Finally, Sect. 5 summarizes the main findings, discussing their implications and suggesting directions for future research.

In this study, five different decadal forecast systems contributing to the Decadal Climate Prediction Project (DCPP; ) of the Coupled Model Intercomparison Project Phase 6 (CMIP6; ), are used. These forecast systems are the EC-Earth3 (with three different initialization strategies), IPSL-CM6A-LR and MPI-ESM1.2-HR. In addition, to analyze the impact of initialization, the indicators are computed for the three corresponding CMIP6 non-initialized historical forcing simulation models (7 EC-Earth3 members, 11 IPSL-CM6A-LR members, and 2 MPI-ESM1.2-HR members). The main characteristics of the DCPP forecast systems and the historical simulation models, including ensemble size, spatial resolutions, initialization months (for the DCPP-A), and references, are detailed in Table S1 of the Supplementary Material. Additionally, the ERA5 reanalysis dataset is used as the observation-based reference. This observational reference has been selected taking into account its temporal and spatial coverage and because it is widely used by the energy community .

Depending on the energy source, different temporal resolutions and variables are used. For solar energy indicators, daily values of surface downwelling shortwave radiation (RSDS), near-surface air temperature (TAS), and surface wind speed (SFCWIND) are employed. For wind energy indicators, instantaneous 6-hourly surface wind (6 h-SFCWIND) data are used. As sub-daily data are required to compute the wind indicators , the number of available models is reduced.

Start dates from 1960–2016 are used for decadal predictions, focusing the analysis on forecast years 1–3. Therefore, to match the period, we use the ERA5 data, as well as the historical forcing simulations concatenated with the scenario ssp245 , during 1961–2019. Historical simulations provide data until 2014, and ssp245 is used for the rest of the years (2015–2019).

The analysis focuses on Europe, defined by the spatial domain spanning longitudes 30° W–12° E and latitudes 35–65° N.

To provide information at a spatial scale relevant for regional applications, a statistical downscaling approach is applied to model simulations to increase the horizontal resolution, bringing them to the ERA5 grid (0.25°). This downscaling procedure consists of a combination of spatial interpolation and calibration using Empirical Quantile Mapping (EQM), described below. This method is adopted, as previous studies have shown that more complex downscaling techniques often provide limited added value in terms of predictive skill compared to simpler approaches (e.g., ; ; ), while minimising the risk of introducing artificial skill. To assess the sensitivity of the results to the spatial resolution used for the analysis, all indicators and skill metrics are additionally computed on the native spatial resolution of the coarsest prediction system considered (IPSL-CM6A-LR: 2.5°×1.25°).

After interpolation and before calibration, wind energy indicators are derived from 6 h-SFCWIND data converted to wind speeds at 100 m (6-hWIND). A representative hub height of 100 m is assumed for modern wind turbines . To estimate hub-height winds from surface winds, a power law is applied , using a shear exponent of α=0.143 over land and α=0.11 over water , under the assumption of neutral atmospheric stability.

In addition, ERA5 wind speeds are adjusted using the Global Wind Atlas (GWA) to better represent long-term mean conditions at hub height. Figure S1 shows the climatological ratio between ERA5 and GWA mean 6-hWIND over 1961–2019, which is applied as a multiplicative pointwise correction factor (GWA/ERA5) to adjust the mean wind speed while preserving the temporal variability of the ERA5 data .

Once wind-specific corrections have been applied, model outputs are adjusted to the observed climatology by removing the model mean and replacing it with the corresponding observed mean. These climatologies are computed over a common reference period (1963–2017), defined by the availability of all forecast years included in this study (i.e. 1–3) across the selected start dates (1960–2016). This adjustment reduces the impact of model drift while preserving the temporal variability of the simulations .

The adjusted decadal predictions and historical simulations are then calibrated against ERA5 using EQM . By adjusting empirical quantiles at 1 % intervals (from the 1st to the 99th percentile), EQM corrects the distribution of the variable, including the mean, variance, and higher-order moments. Calibration is performed independently at each grid point, per forecast year and member for decadal predictions, and per calendar year and member for historical simulations. In both cases, empirical distributions are estimated using a 30 d moving window centered on the target date to preserve seasonality while ensuring a sufficient sample size. Both the climatological adjustment and EQM calibration are performed within a leave-one-year-out cross-validation framework. For each target year, the climatological means and EQM transfer function are estimated using all other years in the calibration period. This prevents information from the target year from being used in the calibration and avoids artificial skill inflation . For 6 hWIND, the calibration is performed separately for each time of the day, since wind distributions differ markedly across the diurnal cycle .

The calibrated climate variables are then used to compute the energy indicators. All indicators are calculated separately for each climatological season and for the whole year. We define three indicators: photovoltaic potential (PVpot) for solar energy, capacity factor for wind energy (WCF), and the Number of Energy Droughts (NED). The intermediate metric Number of Effective days (Neff) is first computed for each resource and subsequently combined to derive NED. Indicators are computed for each forecast system and for the ERA5 reanalysis, which we use as a reference to assess predictive performance. Results for wind-related indicators are analyzed over land and over sea-adjacent regions, as both onshore and offshore wind resources are of practical interest for current and future wind energy deployment.

Following previous studies , PVpot is computed as a dimensionless metric that combines RSDS, TAS and SFCWIND to represent the performance of photovoltaic cells under ambient environment. PV power generation at a specific location is determined by multiplying PVpot with the nominal installed capacity of that location. The PVpot is mainly driven by RSDS, with adjustments for TAS, which lowers module efficiency at higher temperatures , and SFCWIND, which can partially offset thermal losses through convective cooling. Although Eq. (S1) provides PVpot as a unitless quantity, in this study it is converted to percentage values to facilitate visual interpretation. Even though daily inputs are less precise than hourly due to aggregation, they keep seasonal and annual errors small and avoid the much larger biases seen with monthly data .

WCF is defined as the ratio between the energy actually produced and the energy that would be generated if the turbine operated continuously at its rated power. WCF depends on the turbine’s power curve, which varies by turbine type and site conditions; the standard power curves defined by the International Electrotechnical Commission (IEC) 61400-1 are shown in Fig. S2. In this study, an IEC Class I turbine is adopted as a common reference, as a sensitivity analysis presented no notable differences among the skill performance of the different turbines (not shown). Given the high temporal resolution of turbine power curves and their strong nonlinearity, the use of 6-hourly wind data reduces aggregation bias. Although daily means are often adequate , sub-daily data better preserve variability and yield more accurate WCF estimates . WCF is derived using the R-based CSIndicators package .

PVpot and WCF are defined differently, so their absolute values should not be compared across energy sources. To enable a meaningful cross-resource comparison, Neff is defined as the number of days meeting efficiency-related thresholds, providing a common performance indicator with direct operational relevance.

For solar energy production, Neff efficiency thresholds are defined using a minimum RSDS and a maximum cell temperature (Tcell: ). The RSDS threshold is based on the concept of peak sun hours (PSH), defined as the equivalent number of hours per day with an irradiance of 1000 W m⁻² . A commonly used lower limit for meaningful PV generation is 5 PSH d⁻¹, which corresponds to a daily mean RSDS of 208 W m⁻² . Since higher TAS increases Tcell and reduces PVpot, an upper operating threshold of Tcell = 45 °C is applied (see Eq. S2), at which the PV module works at 90 % of its rated efficiency, with PV generation progressively declining above this threshold .

For wind energy, Neff is defined using both operational and performance criteria. Although IEC Class I turbines operate between cut-in (2 m s⁻¹) and cut-out (25 m s⁻¹) wind speeds, winds just above cut-in typically yield little energy production. Therefore, effectiveness is defined using a minimum threshold of WCF ≥ 25 %, which excludes periods of low-efficiency production and can be associated with economically relevant wind power generation . This threshold is translated into an equivalent minimum wind speed using the turbine power curve, and effective time steps are counted when wind speeds exceed this value while remaining below cut-out. To ensure consistency with solar indicators, 6-hourly wind data are aggregated to daily values, defining a day as effective when at least 3 of 4 time steps meet the threshold (Fig. S3).

Based on the effective number of days in which any energy source can operate efficiently, we define the combined indicator NED, which corresponds to the number of days when neither source produces efficiently. In addition, Neff is calculated separately for each energy source (solar-Neff and wind-Neff).

Once the corresponding indicators are calculated for each forecast year, the forecast years 1–3 are averaged in order to obtain a mean value over the entire forecast period for each forecast system. To ensure a consistent comparison with decadal predictions, a rolling mean with a window length equal to the number of forecast years (3) is applied to the historical simulations and ERA5 time series. Subsequently, multi-model ensembles are constructed for decadal predictions (DCPP) and historical simulations (HIST) following the multi-model mean approach .

The performance of the DCPP is evaluated against the ERA5 reanalysis using the Anomaly Correlation Coefficient (ACC; ). ACC ranges from -1 (perfect inverse correlation) to 1 (perfect correlation). A one-sided t-test is applied to determine if ACC values differ significantly from zero . Following , the effective number of degrees of freedom is used to account for autocorrelation in the time series. To assess the impact of initialization, the residual correlation (ResCorr; ) between the DCPP and ERA5 is calculated relative to the HIST. ResCorr is calculated by linearly removing the variability associated with HIST from both DCPP and ERA5 and correlating the resulting residuals. Its significance is assessed using a two-sided t-test with effective degrees of freedom to account for temporal autocorrelation. Positive values of ResCorr indicate that decadal predictions can capture more observed variability that is not already captured by the historical simulations.

Since the DCPP and HIST ensembles have different numbers of members, we perform an additional sensitivity analysis to assess the impact of ensemble size on the estimated added value of initialization. The analysis is applied to the multi-model mean used throughout the study; therefore, ensemble-size adjustments are performed independently for each model before constructing the multi-model average. For each forecast system, the same number of DCPP and HIST members are selected, corresponding to the minimum ensemble size available in both systems (7 for EC-Earth3, 10 for IPSL-CM6A-LR, and 2 for MPI-ESM1.2-HR). For this analysis, the 7 EC-Earth3 decadal prediction members are taken from the three different initializations for their comparison with the non-initialized EC-Earth3 historical simulations. For each resampling, an equal-member multi-model ensemble is constructed and used to estimate ResCorr. The procedure is repeated 5000 times, and the sensitivity to ensemble size is quantified as the difference in residual correlation: ΔResCorr=ResCorrequal-members-ResCorrfull-ensemble.

The median of the resulting ΔResCorr distribution is used as the central estimate at each grid point, while the 2.5th and 97.5th percentiles define a 95 % bootstrap interval. Regions where this interval does not include zero are considered robustly sensitive to ensemble size.

Additionally, we assess the positive bias in ResCorr associated with the finite size of the HIST ensembles, following . Since the multi-model mean is constructed by averaging each prediction system independently, the HIST ensemble of each model is randomly divided into two equal subsets (3+3 members for EC-Earth3, 5+5 for IPSL-CM6A-LR, and 1+1 for MPI-ESM1.2-HR). For EC-Earth3 and IPSL-CM6A-LR, which have 7 and 11 HIST members, respectively, one member is randomly excluded in each repetition. The corresponding subsets are then averaged across prediction systems to construct two independent multi-model HIST estimates (A and B). For each repetition, a biased ResCorr estimate is calculated using HIST A as the reference for both DCPP and ERA5. An unbiased ResCorr estimate is then calculated using the two independent HIST estimates, with HIST A used as the reference for DCPP and HIST B for ERA5. The finite-ensemble bias is estimated as the difference between these two ResCorr values. The procedure is repeated 5000 times, and the median estimated bias is subtracted from the original ResCorr. The uncertainty associated with the ensemble partitioning is quantified as the standard deviation of the bias estimates across the 5000 repetitions.

Finally, after computing the indicators for each grid point, a regionalization is applied to analyze trends and correlations at the regional scale. For each sub-region, indicators are first spatially area-weighted averaged to obtain a single representative time series, from which regional trends, correlations and ResCorr are calculated. This step is motivated by the known contribution of externally forced trends and low-frequency variability to decadal prediction skill in some variables (e.g. ), and allows us to assess how long-term changes influence the estimated predictability. Trends are estimated using a linear fit, while statistical significance is assessed independently using a modified Mann-Kendall test that accounts for temporal autocorrelation following . Europe is divided in seven sub-regions (Fig. S4), following the regionalization of : Iberia, Western Europe, Mediterranean, Central Europe, Eastern Europe, Scandinavia, Great Britain and Ireland.

The results are presented in three separate sections for each indicator: PVpot, WCF, and NED. The ACC of the indicator between DCPP and ERA5, the ACC between HIST and ERA5, and the ResCorr between DCPP and ERA5 relative to HIST are shown for the annual mean and individual seasons. Equivalent results obtained on the native resolution of the coarsest prediction system are shown in Figs. S5, S10 and S13. For each indicator, the climatology and multiannual standard deviation are presented to provide context on the mean state and variability of the indicator (Figs. S6, S11 and S15). Additionally, regional trends, correlations and ResCorr for each indicator are shown in Figs. 2, 4 and 6. The ACC maps of the underlying variables (RSDS, TAS and SFCWIND) are shown in Fig. S7, while their regional trends are presented in Fig. S9. Finally, the robustness of the ResCorr results is further assessed by examining both the sensitivity to differences in ensemble size between DCPP and HIST and the positive bias associated with the finite size of the HIST ensembles, following the procedures described in Sect. 3. The corresponding results, including the sensitivity to equal-member resampling, the bias-corrected ResCorr, and the uncertainty associated with the finite-ensemble correction, are presented in Figs. S8, S12 and S17.

The renewable energy sector is strongly influenced by variability and long-term changes in key climate variables such as temperature, solar radiation and wind speed. However, the intermediate timescale of the coming years remains comparatively underexplored, and there is a clear need for tailored climate information to bridge the gap between decadal prediction advances and their application in renewable energy planning.

This study evaluates the predictive skill of a multi-model ensemble of decadal predictions (DCPP) for energy-relevant indicators over Europe, focusing on photovoltaic potential (PVpot), wind capacity factor (WCF), and the compound indicator number of energy droughts (NED) for forecast years 1–3. Skill is assessed using the ACC between the DCPP and ERA5, and the impact of model initialization is estimated with the ResCorr relative to historical simulations. In addition to grid-point analyses, sub-regional mean trends and correlations are examined to explore the relationship between long-term changes and predictive skill.

PVpot shows the highest and most spatially coherent skill among the indicators analyzed. Skill is significantly positive over large parts of the region at the annual scale and during MAM and JJA, while it is weaker and more spatially limited in DJF and SON. The spatial and seasonal patterns of PVpot skill closely follow those of surface downwelling shortwave radiation (RSDS), its main driver. PVpot skill is slightly lower than RSDS skill because the indicator is also modulated by near-surface air temperature (TAS) and surface wind speed (SFCWIND), which act as secondary constraints on predictability.

Initialization enhances PVpot skill in the annual mean and particularly in JJA, mainly by strengthening regions that already exhibit skill in the multi-model ensemble of historical simulations (HIST). In DJF and SON, the added value of initialization is limited and spatially heterogeneous, with localized improvements and degradations.

WCF exhibits lower and more spatially heterogeneous skill than PVpot, reflecting the higher intrinsic variability of wind and its strong sensitivity to synoptic-scale circulation. Significant skill is consistently found over parts of eastern Europe across seasons, while other regions show weaker and more seasonally dependent skill, including central Europe in JJA and northern Europe in MAM and SON. The spatial distribution of WCF skill closely mirrors that of SFCWIND, confirming wind variability as the primary control on WCF predictability.

The impact of initialization is regionally dependent and generally limited. The clearest skill enhancements are found in JJA over parts of eastern Europe, while in other seasons the impact of initialization is weaker and spatially heterogeneous. As for PVpot, initialization mainly improves skill where historical simulations already show some skill. The generally low WCF skill is consistent with the weak agreement between ERA5 and DCPP trends in many regions and seasons, together with the high intrinsic variability of wind.

The compound indicator NED displays predictability characteristics that reflect the combined influence of solar and wind resources. NED skill is highest in JJA, followed by the annual mean and MAM, and is generally limited in DJF and SON. NED predictability does not require simultaneous skill in both components: in some regions it emerges where both solar-Neff and wind-Neff are predictable, while in others it is dominated by a single source. This highlights the importance of considering the relative contribution of both resources when assessing compound energy drought predictability.

Seasonal differences in NED skill reflect the relative contribution of wind and solar components. In JJA, NED predictability is mainly driven by solar-Neff. In DJF, variability over much of Europe is largely driven by wind conditions, while in MAM and SON the relative contribution of both components varies regionally. As a result, the impact of initialization on NED is also seasonally dependent, enhancing skill when the dominant energy source is predictable, while providing little added value otherwise. This is consistent with the broader finding that initialization generally enhances existing predictability rather than creating skill where historical simulations show none. These results demonstrate the potential of decadal predictions to provide actionable information for anticipating energy droughts, although forecast quality remains strongly dependent on season and region.

By evaluating compound energy indicators, explicitly separating total skill from the added value of initialization, and analysing results at the regional scale, this study advances beyond variable-based assessments and provides a more application-oriented understanding of decadal predictability for renewable energy. These findings highlight both the potential and the limitations of decadal predictions for energy-related applications and underscore the need to clearly communicate where and when such information is reliable. In addition, the main conclusions were robust across spatial resolutions, and the ResCorr results were largely insensitive to differences in ensemble size between DCPP and HIST. However, the finite size of the HIST ensembles represents an additional source of uncertainty in ResCorr. Accounting for this finite-ensemble bias generally reduces positive ResCorr values, indicating that the standard estimate may overestimate the added value of initialization in some regions. Nevertheless, the bias correction itself is uncertain due to the small number of members available in each ensemble partition, as reflected by the relatively large standard deviation across partitions. Therefore, the standard and bias-corrected ResCorr estimates should be interpreted with caution.

Several limitations should be noted. ERA5 is the only reference dataset used, and reanalysis uncertainties, especially for wind, may affect skill estimates. However, ERA5 has been identified as one of the most reliable reanalysis datasets for near-surface wind variability, particularly in terms of correlation with observations and representation of temporal variability at turbine-relevant heights , and the Global Wind Atlas has been used to correct it for a more accurate representation. Secondly, the higher resolution obtained through statistical downscaling does not necessarily imply additional local-scale predictability, particularly where relevant processes are not resolved by the original models, such as wind variability in regions with complex orography. This limitation should be considered when interpreting downscaled information for local-scale energy applications. Thirdly, fixed, efficiency-based thresholds, although operationally motivated, may limit sensitivity and transferability across regions or technologies. Moreover, temporal aggregation choices influence the indicators, particularly for wind, given the finer temporal resolution of turbine power curves. In addition, the power-law extrapolation used to estimate 100 m wind speeds assumes neutral stability and may be less representative under non-neutral atmospheric conditions, which could affect the reliability of local-scale decadal predictions. Finally, the ACC and ResCorr metrics capture correlation-based skill but do not fully describe other aspects relevant for operational decision-making, such as reliability or the representation of extreme events. Other metrics might be selected depending on the climate forecast products required by the energy users.

Future work could assess how sensitive the indicators are to the choice of thresholds (e.g. different RSDS or WCF limits), and compare results using alternative reanalyses or observation-based datasets. The framework could also be extended to include electricity demand, which would allow a more comprehensive assessment of supply–demand imbalances and increase the relevance of the indicators for system operators and energy planners. Furthermore, as decadal prediction systems are known to be affected by signal-to-noise problems , future studies could further investigate the impact of signal-to-noise characteristics of the calibrated ensemble or apply correction methods such as the NAO-matching proposed by .