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Figure 2. Change in average air temperature (◦C) after the gridded statistical <t>downscaling</t> with the E-OBS and GloH2O datasets compared to the raw GCM output data for 1981–2014.
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Figure 2. Change in average air temperature (◦C) after the gridded statistical downscaling with the E-OBS and GloH2O datasets compared to the raw GCM output data for 1981–2014.

Journal: Atmosphere

Article Title: Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin

doi: 10.3390/atmos16020229

Figure Lengend Snippet: Figure 2. Change in average air temperature (◦C) after the gridded statistical downscaling with the E-OBS and GloH2O datasets compared to the raw GCM output data for 1981–2014.

Article Snippet: Many of these decisions can now be standardized by international guidelines from the Coordinated Regional Climate Downscaling Experiment (CORDEX-CMIP6), which specify parameters such as spatial domains, spatial resolutions, SSP priority order and some modelling details [14].

Techniques:

Figure 3. Change in maximum average air temperature (◦C) after the gridded statistical downscaling using the E-OBS and GloH2O datasets compared to the raw GCM outputs for 1981–2014.

Journal: Atmosphere

Article Title: Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin

doi: 10.3390/atmos16020229

Figure Lengend Snippet: Figure 3. Change in maximum average air temperature (◦C) after the gridded statistical downscaling using the E-OBS and GloH2O datasets compared to the raw GCM outputs for 1981–2014.

Article Snippet: Many of these decisions can now be standardized by international guidelines from the Coordinated Regional Climate Downscaling Experiment (CORDEX-CMIP6), which specify parameters such as spatial domains, spatial resolutions, SSP priority order and some modelling details [14].

Techniques:

Figure 4. Change in minimum average air temperature (◦C) after the gridded statistical downscaling using the E-OBS and GloH2O datasets compared to the raw GCM output for 1981–2014.

Journal: Atmosphere

Article Title: Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin

doi: 10.3390/atmos16020229

Figure Lengend Snippet: Figure 4. Change in minimum average air temperature (◦C) after the gridded statistical downscaling using the E-OBS and GloH2O datasets compared to the raw GCM output for 1981–2014.

Article Snippet: Many of these decisions can now be standardized by international guidelines from the Coordinated Regional Climate Downscaling Experiment (CORDEX-CMIP6), which specify parameters such as spatial domains, spatial resolutions, SSP priority order and some modelling details [14].

Techniques:

Figure 5. Change in the number of days with negative air temperature after the gridded statistical downscaling using the E-OBS and GloH2O datasets compared to the raw GCM outputs for 1981–2014.

Journal: Atmosphere

Article Title: Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin

doi: 10.3390/atmos16020229

Figure Lengend Snippet: Figure 5. Change in the number of days with negative air temperature after the gridded statistical downscaling using the E-OBS and GloH2O datasets compared to the raw GCM outputs for 1981–2014.

Article Snippet: Many of these decisions can now be standardized by international guidelines from the Coordinated Regional Climate Downscaling Experiment (CORDEX-CMIP6), which specify parameters such as spatial domains, spatial resolutions, SSP priority order and some modelling details [14].

Techniques:

Figure 6. Change in annual precipitation (%) after the gridded statistical downscaling using the E-OBS and GloH2O datasets compared to the raw GCM output for 1981–2014.

Journal: Atmosphere

Article Title: Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin

doi: 10.3390/atmos16020229

Figure Lengend Snippet: Figure 6. Change in annual precipitation (%) after the gridded statistical downscaling using the E-OBS and GloH2O datasets compared to the raw GCM output for 1981–2014.

Article Snippet: Many of these decisions can now be standardized by international guidelines from the Coordinated Regional Climate Downscaling Experiment (CORDEX-CMIP6), which specify parameters such as spatial domains, spatial resolutions, SSP priority order and some modelling details [14].

Techniques:

Figure 7. Change in maximum precipitation (%) after the gridded statistical downscaling using the E-OBS and GloH2O datasets compared to the raw GCM output for 1981–2014.

Journal: Atmosphere

Article Title: Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin

doi: 10.3390/atmos16020229

Figure Lengend Snippet: Figure 7. Change in maximum precipitation (%) after the gridded statistical downscaling using the E-OBS and GloH2O datasets compared to the raw GCM output for 1981–2014.

Article Snippet: Many of these decisions can now be standardized by international guidelines from the Coordinated Regional Climate Downscaling Experiment (CORDEX-CMIP6), which specify parameters such as spatial domains, spatial resolutions, SSP priority order and some modelling details [14].

Techniques:

Figure 8. Change in the number of dry days (days with zero precipitation) after statistical grid downscaling using the E-OBS and GloH2O datasets compared to the raw GCM outputs for 1981–2014.

Journal: Atmosphere

Article Title: Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin

doi: 10.3390/atmos16020229

Figure Lengend Snippet: Figure 8. Change in the number of dry days (days with zero precipitation) after statistical grid downscaling using the E-OBS and GloH2O datasets compared to the raw GCM outputs for 1981–2014.

Article Snippet: Many of these decisions can now be standardized by international guidelines from the Coordinated Regional Climate Downscaling Experiment (CORDEX-CMIP6), which specify parameters such as spatial domains, spatial resolutions, SSP priority order and some modelling details [14].

Techniques:

Figure 9. Model biases expressed by standardized deviations after gridded statistical downscaling compared to the raw simulation in the study area for 1981–2014, based on the downscaled air temperature and precipitation parameter data according to the historical datasets of E-OBS and GloH2O. The aggregated score represents the sum of the standardized deviations based on the analyzed parameters, the number indicates the consecutive rank of aggregated scores of the individual models according to the order from the lowest bias to the highest in terms of all models.

Journal: Atmosphere

Article Title: Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin

doi: 10.3390/atmos16020229

Figure Lengend Snippet: Figure 9. Model biases expressed by standardized deviations after gridded statistical downscaling compared to the raw simulation in the study area for 1981–2014, based on the downscaled air temperature and precipitation parameter data according to the historical datasets of E-OBS and GloH2O. The aggregated score represents the sum of the standardized deviations based on the analyzed parameters, the number indicates the consecutive rank of aggregated scores of the individual models according to the order from the lowest bias to the highest in terms of all models.

Article Snippet: Many of these decisions can now be standardized by international guidelines from the Coordinated Regional Climate Downscaling Experiment (CORDEX-CMIP6), which specify parameters such as spatial domains, spatial resolutions, SSP priority order and some modelling details [14].

Techniques: