Individual Model Simulations Research Articles

Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0

Abstract. Earth system models (ESMs) are important tools to improve our understanding of present-day climate and to project climate change under different plausible future scenarios. Thus, ESMs are continuously improved and extended, resulting in more complex models. Particularly during the model development phase, it is important to continuously monitor how well the historical climate is reproduced and to systematically analyze, evaluate, understand, and document possible shortcomings. Hence, putting model biases relative to observations or, for example, a well-characterized pre-industrial control run, into the context of deviations shown by other state-of-the-art models greatly helps to assess which biases need to be addressed with higher priority. Here, we introduce the new capability of the open-source community-developed Earth System Model Evaluation Tool (ESMValTool) to monitor running simulations or benchmark existing simulations with observations in the context of results from the Coupled Model Intercomparison Project (CMIP). To benchmark model output, ESMValTool calculates metrics such as the root-mean-square error, the Pearson correlation coefficient, or the earth mover's distance relative to reference datasets. This is directly compared to the same metric calculated for an ensemble of models such as the one provided by Phase 6 of the CMIP (CMIP6), which provides a statistical measure for the range of values that can be considered typical of state-of-the-art ESMs. Results are displayed in different types of plots, such as map plots or time series, with different techniques such as stippling (maps) or shading (time series) used to visualize the typical range of values for a given metric from the model ensemble used for comparison. While the examples shown here focus on atmospheric variables, the new functionality can be applied to any other ESM component such as land, ocean, sea ice, or land ice. Automatic downloading of CMIP results from the Earth System Grid Federation (ESGF) makes application of ESMValTool for benchmarking of individual model simulations, for example, in preparation of Phase 7 of the CMIP (CMIP7), easy and very user-friendly.

Effects of multi-observations uncertainty and models similarity on climate change projections

Climate change projections (CCPs) are based on the multimodel means of individual climate model simulations that are assumed to be independent. However, model similarity leads to projections biased toward the largest set of similar models and intermodel uncertainty underestimation. We assessed the influences of similarities in CMIP6 through CMIP3 CCPs. We ascertained model similarity from shared physics/dynamics and initial conditions by comparing simulated spatial temperature and precipitation with the corresponding observed patterns and accounting for intermodel spread relative to the observational uncertainty, which is also critical. After accounting for similarity, the information from 57 CMIP6, 47 CMIP5, and 24 CMIP3 models can be explained by just 11 independent models without significant differences in globally averaged climate change statistics. On average, independent models indicate a lower global-mean temperature rise of 0.25 °C (~0.5 °C–1 °C in some regions) relative to all models by the end of the 21st century under CMIP6’s highest emission scenario.

The future of the El Niño–Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences

Abstract. Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and individual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time-evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find nonlinear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient, although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single-forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single-forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The nonlinear changes in ENSO SST variability found in many models demonstrate the importance of characterizing this time-dependent behavior, as it implies that ENSO impacts may vary dramatically throughout the 21st century.

ChAP 1.0: a stationary tropospheric sulfur cycle for Earth system models of intermediate complexity

Abstract. A stationary, computationally efficient scheme ChAP 1.0 (Chemical and Aerosol Processes, version 1.0) for the sulfur cycle in the troposphere is developed. This scheme is designed for Earth system models of intermediate complexity (EMICs). The scheme accounts for sulfur dioxide emissions into the atmosphere, its deposition to the surface, oxidation to sulfates, and dry and wet deposition of sulfates on the surface. The calculations with the scheme are forced by anthropogenic emissions of sulfur dioxide into the atmosphere for 1850–2000 adopted from the CMIP5 dataset and by the ERA-Interim meteorology assuming that natural sources of sulfur into the atmosphere remain unchanged during this period. The ChAP output is compared to changes of the tropospheric sulfur cycle simulations with the CMIP5 data, with the IPCC TAR ensemble, and with the ACCMIP phase II simulations. In addition, in regions of strong anthropogenic sulfur pollution, ChAP results are compared to other data, such as the CAMS reanalysis, EMEP MSC-W, and individual model simulations. Our model reasonably reproduces characteristics of the tropospheric sulfur cycle known from these information sources. In our scheme, about half of the emitted sulfur dioxide is deposited to the surface, and the rest is oxidised into sulfates. In turn, sulfates are mostly removed from the atmosphere by wet deposition. The lifetimes of the sulfur dioxide and sulfates in the atmosphere are close to 1 and 5 d, respectively. The limitations of the scheme are acknowledged, and the prospects for future development are figured out. Despite its simplicity, ChAP may be successfully used to simulate anthropogenic sulfur pollution in the atmosphere at coarse spatial scales and timescales.

Economic Impact Analysis of the Application of Different Pavement Performance Models on First-Class Roads with Selected Repair Technology

Mathematical expression of the deterioration of individual pavement parameters is, from the point of optimal repair and maintenance strategy decision-making process, an important part of the application of any pavement management system (PMS). The reliability of individual PMS depends on the quality of the inputs and the reliability of its internal sub-systems; thus, deterioration equations derived from high-quality input data play pivotal roles in a system for the prediction of the pavement life cycle. This paper describes the application of pavement performance models within pavement life cycle analysis (LCA) with the use of the integrated system of economic evaluation (ISEH), which is a calculation tool used for first-class roads with a standardized pavement composition of asphalt binders, where changes in operational capability parameters are modeled using individual model simulations. The simulations presented in this paper demonstrate changes in main economic indicators (net present value and internal rate of return) on two different pavement performance models. Both simulations share the same input parameters (traffic intensity, construction intervention, maintenance costs, discount rate) but differ in deterioration evaluation, all of which were applied to each model (a total of five models).

Overview of the Jack Rabbit II (JR II) field experiments and summary of the methods used in the dispersion model comparisons

Overview of the Jack Rabbit II (JR II) field experiments and summary of the methods used in the dispersion model comparisons

The unidentified eruption of 1809: a climatic cold case

Abstract. The “1809 eruption” is one of the most recent unidentified volcanic eruptions with a global climate impact. Even though the eruption ranks as the third largest since 1500 with a sulfur emission strength estimated to be 2 times that of the 1991 eruption of Pinatubo, not much is known of it from historic sources. Based on a compilation of instrumental and reconstructed temperature time series, we show here that tropical temperatures show a significant drop in response to the ∼ 1809 eruption that is similar to that produced by the Mt. Tambora eruption in 1815, while the response of Northern Hemisphere (NH) boreal summer temperature is spatially heterogeneous. We test the sensitivity of the climate response simulated by the MPI Earth system model to a range of volcanic forcing estimates constructed using estimated volcanic stratospheric sulfur injections (VSSIs) and uncertainties from ice-core records. Three of the forcing reconstructions represent a tropical eruption with an approximately symmetric hemispheric aerosol spread but different forcing magnitudes, while a fourth reflects a hemispherically asymmetric scenario without volcanic forcing in the NH extratropics. Observed and reconstructed post-volcanic surface NH summer temperature anomalies lie within the range of all the scenario simulations. Therefore, assuming the model climate sensitivity is correct, the VSSI estimate is accurate within the uncertainty bounds. Comparison of observed and simulated tropical temperature anomalies suggests that the most likely VSSI for the 1809 eruption would be somewhere between 12 and 19 Tg of sulfur. Model results show that NH large-scale climate modes are sensitive to both volcanic forcing strength and its spatial structure. While spatial correlations between the N-TREND NH temperature reconstruction and the model simulations are weak in terms of the ensemble-mean model results, individual model simulations show good correlation over North America and Europe, suggesting the spatial heterogeneity of the 1810 cooling could be due to internal climate variability.

North Atlantic climate far more predictable than models imply.

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1-3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7-9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.

The impacts of a warming climate on winter mid-latitude cyclones in the NARCCAP model suite

The North American Regional Climate Change Assessment Program (NARCCAP) represents the next step in investigating the behavior of weather phenomena in current and future climate. Specifically, variations in mid-latitude cyclone tracks attributable to a warming climate have potential socio-economic consequences via the redistribution of precipitation on regional spatial scales. This manuscript assesses the impacts of a warming climate on cyclone tracks in the NARCCAP model suite. Specifically, cyclones are generated from eight 33-year simulations for the twentieth Century (1968–2000) and eight 33-year simulations for the A2 greenhouse scenario (2068–2100). To provide comparison, cyclone tracks are also generated from the Climate Forecast System Reanalysis (CFSR) and ERA Interim (ERA_INT) reanalysis datasets from 1979 to 2016. The results support that the NARCCAP model suite is capable of producing a reasonable cyclone frequency and intensity climatology when compared with the reanalysis datasets. Comparison of the ensemble twentieth Century cyclone (20C) tracks with the ensemble A2 cyclone tracks demonstrate a zonally-oriented poleward shift in cyclone track frequency in response to a warming climate. Intensity differences were regionally oriented, with cyclones being more intense in the A2 relative to the twentieth Century scenarios west of the Appalachians, suggesting cyclones acquire greater latent heat from warmer western Atlantic/Gulf of Mexico moisture sources in A2. A sector analysis revealed a higher total frequency of cyclones in both reanalysis datasets relative to either NARCCAP scenario. For intense cyclones, the NARCCAP model simulations produced more frequent cyclones in the Great Lakes and East Coast sectors. In contrast, reanalysis produced a higher frequency of weak cyclones for all sectors except Atlantic Canada. In addition, NARCCAP simulations were found to be capable of reproducing the broadness of intensity distributions in the reanalysis datasets, likely due to the fine spatial gridding in the NARCCAP models. Sector analysis for frequency and intensity affirmed the ensemble results, with individual model simulations showed a reduction in frequency for all sectors except the Canadian Maritimes for A2 relative to 20C. For intensity, cyclones were once again overall more intense for the upper Midwest/Great Lakes sector for A2 relative to 20C. The results of this research demonstrates the ability for regional climate models to be used to assess changes in synoptic-scale phenomena in a warming climate. Future work will focus on assessing cyclone structure including changes in moisture transport, precipitation, and the low-level jet.

Teleconnections and relationship between the El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) in reconstructions and models over the past millennium

Abstract. The climate of the Southern Hemisphere (SH) is strongly influenced by variations in the El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). Because of the limited length of instrumental records in most parts of the SH, very little is known about the relationship between these two key modes of variability over time. Using proxy-based reconstructions and last-millennium climate model simulations, we find that ENSO and SAM indices are mostly negatively correlated over the past millennium. Pseudo-proxy experiments indicate that currently available proxy records are able to reliably capture ENSO–SAM relationships back to at least 1600 CE. Palaeoclimate reconstructions show mostly negative correlations back to about 1400 CE. An ensemble of last-millennium climate model simulations confirms this negative correlation, showing a stable correlation of approximately −0.3. Despite this generally negative relationship we do find intermittent periods of positive ENSO–SAM correlations in individual model simulations and in the palaeoclimate reconstructions. We do not find evidence that these relationship fluctuations are caused by exogenous forcing nor by a consistent climate pattern. However, we do find evidence that strong negative correlations are associated with strong positive (negative) anomalies in the Interdecadal Pacific Oscillation and the Amundsen Sea Low during periods when SAM and ENSO indices are of opposite (equal) sign.

Comparing global hydrological models and combining them with GRACE by dynamic model data averaging (DMDA)

Comparing global hydrological models and combining them with GRACE by dynamic model data averaging (DMDA)

An El Niño Mode in the Glacial Indian Ocean?

Abstract Despite minor variations in sea surface temperature (SST) compared to other tropical regions, coupled ocean‐atmosphere dynamics in the Indian Ocean cause widespread drought, wildfires, and flooding. It is unclear whether changes in the Indian Ocean mean state can support stronger SST variability and climatic extremes. Here we focus on the Last Glacial Maximum (19,000–21,000 years before present) when background oceanic conditions could have been favorable for stronger variability. Using individual foraminiferal analyses and climate model simulations, we find that seasonal and interannual SST variations in the eastern equatorial Indian Ocean were much larger during this glacial period relative to modern conditions. The increase in year‐to‐year variance is consistent with the emergence of an equatorial mode of climate variability, which strongly resembles the Pacific El Niño and is currently not active in the Indian Ocean.

Investigating the causes of increased 20th-century fall precipitation over the southeastern United States.

Much of the eastern United States (US) experienced increased precipitation over the 20th century. Characterizing these trends and their causes is critical for assessing future hydroclimate risks. Here, US precipitation trends are analyzed during 1895-2016, revealing that fall precipitation in the southeastern region north of the Gulf of Mexico (SE-Gulf) increased by nearly 40%, primarily increasing after the mid-1900s. As fall is the climatological dry season in the SE-Gulf and precipitation in other seasons changed insignificantly, the seasonal precipitation cycle diminished substantially. The increase in SE-Gulf fall precipitation was caused by increased southerly moisture transport from the Gulf of Mexico, which was almost entirely driven by stronger winds associated with enhanced anticyclonic circulation west of the North Atlantic Subtropical High (NASH) and not by increases in specific humidity. Atmospheric models forced by observed SSTs and fully-coupled models forced by historical anthropogenic forcing do not robustly simulate 20th-century fall wetting in the SE-Gulf. SST-forced atmospheric models do simulate an intensified anticyclonic low-level circulation around the NASH, but the modeled intensification occurred farther west than observed. CMIP5 analyses suggest an increased likelihood of positive SE-Gulf fall precipitation trends given historical and future GHG forcing. Nevertheless, individual model simulations (both SST-forced and fully-coupled) only very rarely produce the observed magnitude of the SE-Gulf fall precipitation trend. Further research into model representation of the western ridge of the fall NASH is needed, which will help us better predict whether 20th-century increases in SE-Gulf fall precipitation will persist into the future.

Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

Impact of Hydrological Modellers’ Decisions and Attitude on the Performance of a Calibrated Conceptual Catchment Model: Results from a ‘Modelling Contest’

In this study, 17 hydrologists with different experience in hydrological modelling applied the same conceptual catchment model (HBV) to a Greek catchment, using identical data and model code. Calibration was performed manually. Subsequently, the modellers were asked for their experience, their calibration strategy, and whether they enjoyed the exercise. The exercise revealed that there is considerable modellers’ uncertainty even among the experienced modellers. It seemed to be equally important whether the modellers followed a good calibration strategy, and whether they enjoyed modelling. The exercise confirmed previous studies about the benefit of model ensembles: Different combinations of the simulation results (median, mean) outperformed the individual model simulations, while filtering the simulations even improved the quality of the model ensembles. Modellers’ experience, decisions, and attitude, therefore, have an impact on the hydrological model application and should be considered as part of hydrological modelling uncertainty.

Extending CMIP5 projections of global mean temperature change and sea level rise due to thermal expansion using a physically-based emulator

We present a physically-based emulator approach to extending 21st century CMIP5 model simulations of global mean surface temperature (GMST) and global thermal expansion (TE) to 2300. A two-layer energy balance model that has been tuned to emulate the CO2 response of individual CMIP5 models is combined with model-specific radiative forcings to generate an emulated ensemble to 2300 for RCP2.6, RCP4.5 and RCP8.5. Errors in the emulated time series are quantified using a subset of CMIP5 models with data available to 2300 and factored into the ensemble uncertainty. The resulting projections show good agreement with 21st century ensemble projections reported in IPCC AR5 and also compare favourably with individual CMIP5 model simulations post-2100. There is a tendency for the two-layer model simulations to overestimate both GMST rise and TE under RCP2.6, which is suggestive of a systematic error in the applied radiative forcings. Overall, the framework shows promise as a basis for extending process-based projections of global sea level rise beyond the 21st century time horizon that typifies CMIP5 simulations. The results also serve to illustrate the differing responses of GMST and Earth’s energy imbalance (EEI) to reductions in greenhouse gas emissions. GMST responds relatively quickly to changes in emissions, leading to a negative trend post-2100 for RCP2.6, although temperature remains substantially elevated compared to present day at 2300. In contrast, EEI remains positive under all RCPs, and results in ongoing sea level rise from TE.

Evaluating Global Land Surface Models in CMIP5: Analysis of Ecosystem Water- and Light-Use Efficiencies and Rainfall Partitioning

Abstract Water and carbon fluxes simulated by 12 Earth system models (ESMs) that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) over several recent decades were evaluated using three functional constraints that are derived from both model simulations, or four global datasets, and 736 site-year measurements. Three functional constraints are ecosystem water-use efficiency (WUE), light-use efficiency (LUE), and the partitioning of precipitation P into evapotranspiration (ET) and runoff based on the Budyko framework. Although values of these three constraints varied significantly with time scale and should be quite conservative if being averaged over multiple decades, the results showed that both WUE and LUE simulated by the ensemble mean of 12 ESMs were generally lower than the site measurements. Simulations by the ESMs were generally consistent with the broad pattern of energy-controlled ET under wet conditions and soil water-controlled ET under dry conditions, as described by the Budyko framework. However, the value of the parameter in the Budyko framework ω, obtained from fitting the Budyko curve to the ensemble model simulation (1.74), was larger than the best-fit value of ω to the observed data (1.28). Globally, the ensemble mean of multiple models, although performing better than any individual model simulations, still underestimated the observed WUE and LUE, and overestimated the ratio of ET to P, as a result of overestimation in ET and underestimation in gross primary production (GPP). The results suggest that future model development should focus on improving the algorithms of the partitioning of precipitation into ecosystem ET and runoff, and the coupling of water and carbon cycles for different land-use types.

Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models

It is known that internal climate variability (ICV) can influence trends seen in observations and individual model simulations over a period of decades. This makes it difficult to quantify the forced response to external forcing. Here we analyze two large ensembles of simulations from 1950 to 2100 by two fully-coupled climate models, namely the CESM1 and CanESM2, to quantify ICV’s influences on estimated trends in annual surface air temperature (Tas) and precipitation (P) over different time periods. Results show that the observed trends since 1979 in global-mean Tas and P are within the spread of the CESM1-simulated trends while the CanESM2 overestimates the historical changes, likely due to its deficiencies in simulating historical non-CO2 forcing. Both models show considerable spreads in the Tas and P trends among the individual simulations, and the spreads decrease rapidly as the record length increases to about 40 (50) years for global-mean Tas (P). Because of ICV, local and regional P trends may remain statistically insignificant and differ greatly among individual model simulations over most of the globe until the later part of the twenty-first century even under a high emissions scenario, while local Tas trends since 1979 are already statistically significant over many low-latitude regions and are projected to become significant over most of the globe by the 2030s. The largest influences of ICV come from the Inter-decadal Pacific Oscillation and polar sea ice. In contrast to the realization-dependent ICV, the forced Tas response to external forcing has a temporal evolution that is similar over most of the globe (except its amplitude). For annual precipitation, however, the temporal evolution of the forced response is similar (opposite) to that of Tas over many mid-high latitude areas and the ITCZ (subtropical regions), but close to zero over the transition zones between the regions with positive and negative trends. The ICV in the transient climate change simulations is slightly larger than that in the control run for P (and other related variables such as water vapor), but similar for Tas. Thus, the ICV for P from a control run may need to be scaled up in detection and attribution analyses.

Comment on “Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations”

In a recent article, Cheung et al. applied a semiempirical methodology to isolate internal climate variability (ICV) in CMIP5 models and observations. The essence of their methodology is to subtract the scaled CMIP5 multimodel ensemble mean (MMEM) from individual model simulations and from the observed time series of several surface temperature indices. Cheung et al. detected large differences in both the magnitude and spatial patterns of the observed and simulated ICV, as well as large differences between the historical (simulated) ICV and preindustrial (PI) control CMIP5 simulations. Here it is shown that subtraction of the scaled MMEM from CMIP5 historical simulations produces a poor estimate of the modeled ICV due to the difference between the scaled MMEM and a given model’s true forced signal masquerading as ICV. The resulting phase and amplitude errors of the ICV so estimated are large, which compromises most of Cheung et al.’s conclusions pertaining to characterization of ICV in the historical CMIP5 simulations. By contrast, an alternative methodology based on forced signals computed from individual model ensembles produces a much more accurate estimate of the ICV in CMIP5 models, whose magnitude is consistent with the PI control simulations and is much smaller than any of the semiempirical estimates of the observed ICV on decadal and longer time scales.

CHASE-PL Climate Projection dataset over Poland – bias adjustment of EURO-CORDEX simulations

Abstract. The CHASE-PL (Climate change impact assessment for selected sectors in Poland) Climate Projections – Gridded Daily Precipitation and Temperature dataset 5 km (CPLCP-GDPT5) consists of projected daily minimum and maximum air temperatures and precipitation totals of nine EURO-CORDEX regional climate model outputs bias corrected and downscaled to a 5 km × 5 km grid. Simulations of one historical period (1971–2000) and two future horizons (2021–2050 and 2071–2100) assuming two representative concentration pathways (RCP4.5 and RCP8.5) were produced. We used the quantile mapping method and corrected any systematic seasonal bias in these simulations before assessing the changes in annual and seasonal means of precipitation and temperature over Poland. Projected changes estimated from the multi-model ensemble mean showed that annual means of temperature are expected to increase steadily by 1 °C until 2021–2050 and by 2 °C until 2071–2100 assuming the RCP4.5 emission scenario. Assuming the RCP8.5 emission scenario, this can reach up to almost 4 °C by 2071–2100. Similarly to temperature, projected changes in regional annual means of precipitation are expected to increase by 6 to 10 % and by 8 to 16 % for the two future horizons and RCPs, respectively. Similarly, individual model simulations also exhibited warmer and wetter conditions on an annual scale, showing an intensification of the magnitude of the change at the end of the 21st century. The same applied for projected changes in seasonal means of temperature showing a higher winter warming rate by up to 0.5 °C compared to the other seasons. However, projected changes in seasonal means of precipitation by the individual models largely differ and are sometimes inconsistent, exhibiting spatial variations which depend on the selected season, location, future horizon, and RCP. The overall range of the 90 % confidence interval predicted by the ensemble of multi-model simulations was found to likely vary between −7 % (projected for summer assuming the RCP4.5 emission scenario) and +40 % (projected for winter assuming the RCP8.5 emission scenario) by the end of the 21st century. Finally, this high-resolution bias-corrected product can serve as a basis for climate change impact and adaptation studies for many sectors over Poland. The CPLCP-GDPT5 dataset is publicly available at http://dx.doi.org/10.4121/uuid:e940ec1a-71a0-449e-bbe3-29217f2ba31d.