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Downscaling & Bias correction of climate simulations

Figure: Conceptual view on downscaling. The goal is to generate high-resolution simulations, based on lower-resolution data (coming from reanalyses or global climate simulations)

Global Climate Models (GCMs) are the primary tools to simulate multi-decadal climate dynamics and to generate and understand global climate change projections under different future emission scenarios. However, these models have a coarse spatial resolution (typically a few hundred kilometres) and suffer from substantial systematic biases when compared with observations. Therefore, they are unable to provide actionable information at the regional and local spatial scales required in impact and adaptation studies (e.g., Grouillet al., 2016). Hence, higher‐resolution simulations – and if possible bias corrected – are required for the most relevant climate variables (Vaittinada Ayar et al., 2015).

Downscaling attempts to resolve the scale discrepancy between climate change scenarios and the resolution required for impact assessment. Two main downscaling approaches have been developed since the early 1990s: Dynamical downscaling (based on Regional Climate Models, RCMs) and Statistical Downscaling Models (SDMs), which are nowadays recognized as complementary in many practical applications.

The ESTIMR team develops, evaluates and applies both approaches.

For the first time, high-resolution (12km), century-scale simulations over Europe using the WRF model have been carried out (Vautard et al., 2013; Jacob et al., 2014), and have been used in a series of subsequent studies for evaluation of the added value of high resolution against observations (e.g., Prein et al, 2016; Casanueva et al., 2015), for assessing climate change and impacts (e.g., Jacob et al., 2017; Jerez et al, 2015; Tobin et  al., 2018).

We have developed various SDMs and bias correction (BC) methods, even combined sometimes (Volosciuk et al., 2017), which improves the downscaling results (Vrac and Vaittinada Ayar, 2016). The developed SDMs mostly belong to the “stochastic weather generators” (SWG) family (e.g., Eden et al, 2014) with some focuses on extremes (Wong et al., 2014) and their spatial modelling (Bechler et al., 2015).

Key efforts have been also put on specific BC developments for precipitation (Vrac et al., 2016) and for multivariate (i.e., multi-sites and variables) approaches, both in downscaling and BC contexts (Vrac and Friederichs, 2015; Vrac, 2018).

Note that the CDF-t bias correction method, developed in ESTIMR, is being implemented to run operationally at the IPSL computing centre. This opens the way to automatic BC of the upcoming CMIP6 simulations.

Those various downscaling and bias correction approaches have been applied in different contexts: for example, to drive hydrological models in the Mediterranean basin (Grouillet et al., 2016); to provide future scenarios of precipitation and water availability over India (Vigaud et al., 2013); to investigate potential consequences of rapid ice-sheet melting on Sahelian population (Defrance et al., 2017); or even downscaling of paleao-climate simulations during last glacial maximum (Burke et al., 2014, 2017).

Moreover, within the StaRMIP project, a guidelines study was performed to evaluate both SDMs and RCMs simulations according to statistical properties required to drive impact models (Vaittinada Ayar et al., 2015). We also participated to the European VALUE intercomparison of SDMs, focusing on various aspects (e.g., cross-validation: Guttierez et al., 2018; extremes: Hertig et al., 2018). Moving from climate to weather, methodological advances concerning correction of weather forecasts have also been done with colleagues from Meteo France (Taillardart et al., 2016, Zamo and Naveau 2016).




Bechler, A., Vrac, M., Bel, L. (2015). A spatial hybrid approach for downscaling of extreme precipitation fields. Journal of Geophysical Research - Atmospheres, 120 (10), 4534-4550. DOI : 10.1002/2014JD022558

Burke, A., G. Levavasseur, P. James, D. Guiducci, M. Izquierdo, L. Bourgeon, M. Kageyama, G. Ramstein and M. Vrac (2014). Exploring the impact of climate variability on the pattern of human occupation of Iberia during the Last Glacial Maximum. Journal of Human Evolution, Vol. 73, 35-46,

Burke, A., Kageyama, M., Latombe, G., Fasel, M., Vrac, M., Ramstein, G., James, P. (2017) Risky business: the impact of climate and climate variability on human population dynamics in Western Europe during the Las Glacial Maximum. Quaternary Science Reviews, Volume 164, Pages 217−229, ISSN 0277−3791,

Casanueva, A. +13 authors, 2015: daily precipitation statistics in the EURO-CORDEX RCM ensemble: Added value of a high resolution and implications for bias correction. Climate Dynamics, doi:10.​1007/​s00382-015-2865-x

Defrance, D., Ramstein, G., Charbit, S., Vrac, M., Adjoua, M.F., Sultan, B., Swingedouw, D., Dumas, C., Gemenne, F., Alvarez-Solas, J., and Vanderlinden, J.-P. (2017) Consequences of rapid ice-sheet melting on the Sahelian population vulnerability. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 114 (25), pp. 6533−6538, doi: 10.1073/pnas.1619358114

Eden, J., M. Widmann, D. Maraun, M. Vrac, (2014). Comparison of GCM- and RCM-simulated precipitation following stochastic postprocessing. Journal of Geophysical Research - Atmospheres, 119, 11,04011,053, doi:10.1002/2014JD021732.

Grouillet, B., Ruelland, D., Vaittinada Ayar, P., Vrac, M. (2016) Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean. Hydrology and Earth System Sciences, 20, 1031-1047, doi: 10.5194/hess-20-1031-2016

Guttierez et al. (+37 authors, including M. Vrac) (2018) An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment. In press in International Journal of Climatology

Hertig, E. et al., (+8 authors, including M. Vrac) (2018) Validation of extremes from the Perfect-Predictor Experiment of the COST Action VALUE. In press in Internat. J. of Climatol.

Jacob, D. +37 authors, 2014, EURO-CORDEX: New high-resolution climate change projections for European impact research, Regional Environmental change, 14, 563-578.

Jacob, D., +11 authors (2017): Climate impacts in Europe under +1.5oC global warming, Earth’s Future, doi: 10.1002/2017EF000710

Jerez, S., Thais, F., Tobin, I., Wild, M., Colette, A., Yiou, P., and R. Vautard, 2015: The CLIMIX model: a tool to create and evaluate spatially-resolved scenarios of photovoltaic and wind power development. Renewable and sustainable energy reviews, 42, 1-15.

Prein, A. +12 authors, 2016: Precipitation in the EURO-CORDEX 0.11° and 0.44° simulations: high resolution, high benefits? Climate Dynamics, 46(1-2), 383-412.

Taillardat M., O. Mestre, M. Zamo, P. Naveau. Calibrated Ensemble Forecasts using Quantile Regression Forests and Ensemble Model Output Statistics. Monthly Weather Review, DOI 10.1007/s00382-016-3079-6, 2016.

Tobin, I., Greuell W., Jerez S., Ludwig F., Vautard R., van Vliet M.T.H., and Bréon F.-M., 2018: Vulnerabilities and resilience of European power generation to 1.5°C, 2°C and 3°C warming, Environ. Res. Lett., in press.

Vaittinada Ayar P., Vrac M., Bastin S., Carreau J., Déqué M., Gallardo C. (2016) Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: Present climate evaluations. Clim. Dyn. 46, 1301-1329,

Vautard, R., + 25 authors, 2013: The simulation of European heat waves from an ensemble of regional climate models within the EURO-CORDEX project. Climate Dynamics, 41, 2555-2575

Vigaud, N., M. Vrac, Y. Caballero (2013): Probabilistic Downscaling of GCM scenarios over southern India. International Journal of Climatology, Vol. 33, issue 5, 1248-1263, DOI: 10.1002/joc.3509

Volosciuk, C., Maraun, D., Vrac, M. and Widmann, M. (2017) A Combined Statistical Bias Correction and Stochastic Downscaling Method for Precipitation. Hydrol. And Earth Syst. Sci., 21, 1693–1719, doi:10.5194/hess−21−1693−2017

Vrac, M. (2018) Multivariate bias adjustment of high-dimensional climate simulations: The “Rank Resampling for Distributions and Dependences” (R2D2) Bias Correction. Hydrol. Earth Syst. Sci. Discuss.,, in review

Vrac, M., and Friederichs, P. (2015) Multivariate – Inter-variable, spatial and temporal − bias correction. Journal of Climate, 28, 218–237, doi:−D−14−00059.1

Vrac, M., Vaittinada Ayar, P. (2016) Influence of bias correcting predictors on statistical downscaling models. J. of App. Meteo. and Clim., 56, 5–26,−D−16−0079.1

Vrac, M., Noël, T., Vautard, R. (2015) Bias correction of precipitation through Singularity Stochastic Removal: Because Occurrences matter. JGR-Atmosphere, 121 (10), 5237–5258, DOI: 10.1002/2015JD024511

Wong, G., D. Maraun, M. Vrac, M. Widmann, J. Eden, T. Kent (2014). Stochastic model output statistics for bias correcting and downscaling precipitation including extremes. J. Climate, Volume 27, Issue 18, doi:

Zamo M. and P. Naveau. Estimation of the Continuous Ranked Probability Score with Limited Information Mathematical Geosciences, doi: 10.1007/s11004-017-9709-7, 2017.



Maj : 26/03/2018 (161)

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