Figure 1.
Climatological mean annual bias in total annual precipitation from very heavy rain days (R99p: mm) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951—2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level. The white masks in part of the inland domain are the regions with no station data.
Figure 1.
Climatological mean annual bias in total annual precipitation from very heavy rain days (R99p: mm) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951—2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level. The white masks in part of the inland domain are the regions with no station data.
Figure 2.
Climatological mean annual bias in maximum 1-day precipitation (Rx1Day: mm) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level. The white masks in part of the inland domain are the regions with no station data.
Figure 2.
Climatological mean annual bias in maximum 1-day precipitation (Rx1Day: mm) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level. The white masks in part of the inland domain are the regions with no station data.
Figure 3.
Climatological mean annual bias in the number of very heavy rain days (rain > 10 mm) (R10mm: days) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level. The white masks in part of the inland domain are the regions with no station data.
Figure 3.
Climatological mean annual bias in the number of very heavy rain days (rain > 10 mm) (R10mm: days) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level. The white masks in part of the inland domain are the regions with no station data.
Figure 4.
Climatological mean annual bias in consecutive wet days (CWD: days) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level. The white masks in part of the inland domain are the regions with no station data.
Figure 4.
Climatological mean annual bias in consecutive wet days (CWD: days) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level. The white masks in part of the inland domain are the regions with no station data.
Figure 5.
Climatological mean annual bias in consecutive dry days (CDD: days) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level. The white masks in part of the inland domain are the regions with no station data.
Figure 5.
Climatological mean annual bias in consecutive dry days (CDD: days) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level. The white masks in part of the inland domain are the regions with no station data.
Figure 6.
Climatological mean annual bias in maximum maximum temperature (TXx: °C) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 6.
Climatological mean annual bias in maximum maximum temperature (TXx: °C) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 7.
Climatological mean annual bias in the number of days when maximum temperature is greater than 35 C (Txge35: days) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 7.
Climatological mean annual bias in the number of days when maximum temperature is greater than 35 C (Txge35: days) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 8.
Climatological mean annual bias in summer days (number of days when maximum temperature > 25 °C) (SU: days) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 8.
Climatological mean annual bias in summer days (number of days when maximum temperature > 25 °C) (SU: days) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 9.
Climatological mean annual bias in the minimum minimum temperature (TNn: °C) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 9.
Climatological mean annual bias in the minimum minimum temperature (TNn: °C) relative to the Australian Gridded Climate Data dataset (AGCD; panel 1) for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 10.
Climatological mean annual bias in tropical nights (number of days when minimum temperature > 20 °C (TR: days) relative to Australian Gridded Climate Data dataset (AGCD; panel 1), for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 10.
Climatological mean annual bias in tropical nights (number of days when minimum temperature > 20 °C (TR: days) relative to Australian Gridded Climate Data dataset (AGCD; panel 1), for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 11.
Climatological mean annual bias in the annual count of nights with at least 4 consecutive nights when daily minimum temperature < 10th percentile (CSDI: days) relative to Australian Gridded Climate Data dataset (AGCD; panel 1), for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 11.
Climatological mean annual bias in the annual count of nights with at least 4 consecutive nights when daily minimum temperature < 10th percentile (CSDI: days) relative to Australian Gridded Climate Data dataset (AGCD; panel 1), for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 12.
Climatological mean annual bias in the annual count of days with at least 4 consecutive days when daily maximum temperature > 90th percentile (WSDI: days) relative to Australian Gridded Climate Data dataset (AGCD; panel 1), for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 12.
Climatological mean annual bias in the annual count of days with at least 4 consecutive days when daily maximum temperature > 90th percentile (WSDI: days) relative to Australian Gridded Climate Data dataset (AGCD; panel 1), for the individual CMIP6 GCMs (panels 2–38). Data spans 1951–2014. Stippling indicates statistically significant differences using a student’s t-test at the 95% confidence level.
Figure 13.
Normalised and continentally averaged annual and seasonal means of individual metrics (bias, root mean square error (RMSE), pattern correlation (PCorr) and interannual variability score (IVS)) for the extreme indices (a) maximum 1-day precipitation (Rx1Day), (b) number of very heavy rain days (rain > 10 mm) (R10mm), (c) consecutive wet days (CWD), (d) consecutive dry days (CDD), (e) number of days when maximum temperature is greater than 35 °C (Txge35), (f) tropical nights (number of days when minimum temperature > 20 °C) (TR), (g) summer days (number of days when maximum temperature > 25 °C) (SU), (h) maximum maximum-temperature (TXx) and (i) minimum minimum-temperature (TNn). Here, the smaller values of normalised metrics correspond to the better performance of GCMs. Moreover, for each extreme index, GCMs are arranged from best to worst performance as we move from left and right. Data spans 1951–2014.
Figure 13.
Normalised and continentally averaged annual and seasonal means of individual metrics (bias, root mean square error (RMSE), pattern correlation (PCorr) and interannual variability score (IVS)) for the extreme indices (a) maximum 1-day precipitation (Rx1Day), (b) number of very heavy rain days (rain > 10 mm) (R10mm), (c) consecutive wet days (CWD), (d) consecutive dry days (CDD), (e) number of days when maximum temperature is greater than 35 °C (Txge35), (f) tropical nights (number of days when minimum temperature > 20 °C) (TR), (g) summer days (number of days when maximum temperature > 25 °C) (SU), (h) maximum maximum-temperature (TXx) and (i) minimum minimum-temperature (TNn). Here, the smaller values of normalised metrics correspond to the better performance of GCMs. Moreover, for each extreme index, GCMs are arranged from best to worst performance as we move from left and right. Data spans 1951–2014.
Figure 14.
Normalised and continentally averaged annual means of individual metrics (bias, root mean square error (RMSE), pattern correlation (PCorr) and interannual variability score (IVS)) for the extreme indices (a) total annual precipitation from very heavy rain days (R99p), (b) annual count of nights with at least 4 consecutive nights when daily minimum temperature < 10th percentile (CSDI) and (c) annual count of days with at least 4 consecutive days when daily maximum temperature > 90th percentile (WSDI). Here, the smaller values of normalised metrics correspond to the better performance of GCMs. Moreover, for each extreme index, GCMs are arranged from best to worst performance as we move from left and right. Data spans 1951–2014.
Figure 14.
Normalised and continentally averaged annual means of individual metrics (bias, root mean square error (RMSE), pattern correlation (PCorr) and interannual variability score (IVS)) for the extreme indices (a) total annual precipitation from very heavy rain days (R99p), (b) annual count of nights with at least 4 consecutive nights when daily minimum temperature < 10th percentile (CSDI) and (c) annual count of days with at least 4 consecutive days when daily maximum temperature > 90th percentile (WSDI). Here, the smaller values of normalised metrics correspond to the better performance of GCMs. Moreover, for each extreme index, GCMs are arranged from best to worst performance as we move from left and right. Data spans 1951–2014.
Figure 15.
Spatially averaged normalised scores of the GCMs for the extreme indices (a) total annual precipitation from very heavy rain days (R99p), (b) maximum 1-day precipitation (Rx1Day), (c) number of very heavy rain days (rain > 10 mm) (R10mm), (d) consecutive wet days (CWD), (e) consecutive dry days (CDD), (f) number of days when maximum temperature is greater than 35 °C (Txge35), (g) tropical nights (number of days when minimum temperature > 20 °C) (TR), (h) summer days (number of days when maximum temperature > 25 °C) (SU), (i) maximum maximum-temperature (TXx), (j) minimum minimum-temperature (TNn), (k) annual count of nights with at least 4 consecutive nights when daily minimum temperature < 10th percentile (CSDI) and (l) annual count of days with at least 4 consecutive days when daily maximum temperature > 90th percentile (WSDI). Here, the smaller score values correspond to the better performance of GCMs. Also, for each extreme index, GCMs are arranged from best to worst performance as we move from left and right. Here black, blue, red, magenta and cyan colours denote means over the continent, eastern Australia, northern Australia, southern Australia and rangelands. The markers denote the continental (triangle), northern Australia (star), eastern Australia (circle), southern Australia (diamond) and rangelands (square) values. Data spans 1951–2014.
Figure 15.
Spatially averaged normalised scores of the GCMs for the extreme indices (a) total annual precipitation from very heavy rain days (R99p), (b) maximum 1-day precipitation (Rx1Day), (c) number of very heavy rain days (rain > 10 mm) (R10mm), (d) consecutive wet days (CWD), (e) consecutive dry days (CDD), (f) number of days when maximum temperature is greater than 35 °C (Txge35), (g) tropical nights (number of days when minimum temperature > 20 °C) (TR), (h) summer days (number of days when maximum temperature > 25 °C) (SU), (i) maximum maximum-temperature (TXx), (j) minimum minimum-temperature (TNn), (k) annual count of nights with at least 4 consecutive nights when daily minimum temperature < 10th percentile (CSDI) and (l) annual count of days with at least 4 consecutive days when daily maximum temperature > 90th percentile (WSDI). Here, the smaller score values correspond to the better performance of GCMs. Also, for each extreme index, GCMs are arranged from best to worst performance as we move from left and right. Here black, blue, red, magenta and cyan colours denote means over the continent, eastern Australia, northern Australia, southern Australia and rangelands. The markers denote the continental (triangle), northern Australia (star), eastern Australia (circle), southern Australia (diamond) and rangelands (square) values. Data spans 1951–2014.
Table 1.
CMIP6 Models and simulations used in this study. Here colours other than black denote GCMs from the same modelling institution.
Table 1.
CMIP6 Models and simulations used in this study. Here colours other than black denote GCMs from the same modelling institution.
| GCM Name | Institution/Centre | Run | Atmosphere Lat/lon Grid (°) |
---|
1. | ACCESS-CM2 | Australian Community | r1i1p1f1 | 1.2 × 1.8 |
2. | ACCESS-ESM1-5 | Australian Community | r1i1p1f1 | 1.2 × 1.8 |
3. | AWI-ESM-1-1-LR | Alfred Wegener Institute | r1i1p1f1 | 0.9 × 0.9 |
4. | BCC-CSM2-MR | Beijing Climate Centre | r1i1p1f1 | 1.1 × 1.1 |
5. | BCC-ESM1 | Beijing Climate Centre | r1i1p1f1 | 2.8 × 2.8 |
6. | CMCC-CM2-SR5 | Euro-Mediterranean Centre | r1i1p1f1 | ~ 0.9 |
7. | CNRM-CM6-1-HR | National Centre of Meteorological Research (NCMR), France | r1i1p1f2 | ~ 0.5 |
8. | CNRM-CM6-1 | National Centre of Meteorological Research (NCMR), France | r1i1p1f2 | 1.4 × 1.4 |
9. | CNRM-ESM2-1 | National Centre of Meteorological Research (NCMR), France | r1i1p1f2 | 1.4 × 1.4 |
10. | CanESM5 | Canadian Centre for Climate Modelling and Analysis | r1i1p1f1 | 2.8 × 2.8 |
11. | EC-Earth3-Veg-LR | EC-EARTH consortium, The Netherlands/Ireland | r1i1p1f1 | 0.7 × 0.7 |
12. | EC-Earth3-Veg | EC-EARTH consortium, The Netherlands/Ireland | r1i1p1f1 | 0.7 × 0.7 |
13. | EC-Earth3 | EC-EARTH consortium, The Netherlands/Ireland | r1i1p1f1 | 0.7 × 0.7 |
14. | FGOALS-f3-L | Chinese Academy of Sciences, China | r1i1p1f1 | 2.3 × 2.0 |
15. | FGOALS-g3 | Chinese Academy of Sciences, China | r1i1p1f1 | 2.3 × 2.0 |
16. | GFDL-CM4 | NOAA Geophysical Fluid Dynamics Laboratory | r1i1p1f1 | 1.0 × 1.3 |
17. | GFDL-ESM4 | NOAA Geophysical Fluid Dynamics Laboratory | r1i1p1f1 | 1.0 × 1.3 |
18. | HadGEM3-GC31-LL | Met Office Hadley Centre, UK | r1i1p1f3 | 2.2 × 2.2 |
19. | HadGEM3-GC31-MM | Met Office Hadley Centre, UK | r1i1p1f3 | 0.9 × 0.9 |
20. | INM-CM4-8 | Institute for Numerical Mathematics (INM), Russia | r1i1p1f1 | 1.5 × 2.0 |
21. | INM-CM5-0 | Institute for Numerical Mathematics (INM), Russia | r1i1p1f1 | 1.5 × 2.0 |
22. | IPSL-CM6A-LR | Institute Pierre Simon Laplace, France | r1i1p1f1 | 1.3 × 2.5 |
23. | KACE-1-0-G | National Institute of Meteorological Sciences/Korea Meteorological Administration | r1i1p1f1 | 2.2 × 2.2 |
24. | KIOST-ESM | Korean Institute of Ocean Science and technology | r1i1p1f1 | 2.2 × 2.2 |
25. | MIR°C-ES2L | National Institute for Environmental Studies, Japan | r1i1p1f2 | 4.5 × 4.5 |
26. | MIR°C6 | National Institute for Environmental Studies, Japan | r1i1p1f1 | 1.4 × 1.4 |
27. | MPI-ESM-1-2-HAM | Max Planck Institute for Meteorology (MPI), Germany | r1i1p1f1 | 2.2 × 2.2 |
28. | MPI-ESM1-2-HR | Max Planck Institute for Meteorology (MPI), Germany | r1i1p1f1 | ~0.9 |
29. | MPI-ESM1-2-LR | Max Planck Institute for Meteorology (MPI), Germany | r1i1p1f1 | ~2.0 |
30. | MRI-ESM2-0 | Meteorological Research Institute, Japan | r1i1p1f1 | 1.1 × 1.1 |
31 | NESM3 | Nanjing University of Information Science and Technology, Nanjing | r1i1p1f1 | 1.9 × 1.9 |
32. | NorCPM1 | Norwegian Climate Centre, Norway | r1i1p1f1 | 1.9 × 2.5 |
33. | NorESM2-LM | Norwegian Climate Centre, Norway | r1i1p1f1 | 1.9 × 2.5 |
34. | NorESM2-MM | Norwegian Climate Centre, Norway | r1i1p1f1 | 0.9 × 0.9 |
35. | SAM0-UNICON | Seoul National University | r1i1p1f1 | 0.9 × 1.3 |
36. | TaiESM1 | Taiwan Earth System Model | r1i1p1f1 | 0.9 × 0.9 |
37. | UKESM1-0-LL | UK Met Office and NERC research centres | r1i1p1f2 | 1.3 × 1.9 |
Table 2.
List of ET-SCI Indices evaluated in this study.
Table 2.
List of ET-SCI Indices evaluated in this study.
| Index | Definition | Units | Timescale | Sectors |
---|
1. | R99p | Annual total precipitation when precipitation is greater than the 99th percentile | mm | Annual | Coasts |
2. | Rx1day | Maximum 1-day precipitation | mm | Annual/Monthly | Agriculture, Forestry |
3. | R10mm | Number of very heavy rain days (rain > 10 mm) | days | Annual/Monthly | coasts |
4. | CWD | Consecutive wet days | days | Annual/Monthly | Agriculture, Food security, Water resources |
5. | CDD | Consecutive dry days | days | Annual/Monthly | Agriculture, Food security, Water resources |
6. | TXge35 | Number of days when maximum temperature is greater than 35 °C | days | Annual/Monthly | Health, Agriculture and Disaster and risk management |
7. | TR | Tropical nights (Number of days when minimum temperature > 20 °C) | days | Annual/Monthly | Health, forestry |
8. | SU | Summer days (Number of days when maximum temperature > 25 °C) | days | Annual/Monthly | Health, forestry |
9. | TXx | Maximum maximum-temperature | °C | Annual/Monthly | Agriculture and food, Energy, forestry |
10. | TNn | Minimum minimum-temperature | °C | Annual/Monthly | Agriculture and food, Energy, forestry |
11. | CSDI | Cold Spell Duration Indicator (Annual count of nights with at least 6 consecutive nights when daily minimum temperature < 10th percentile) | days | Annual | Health, Energy, disaster risk reduction, Agriculture |
12. | WSDI | Warm Spell Duration Indicator (Annual count of days with at least 6 consecutive days when daily maximum temperature > 90th percentile) | days | Annual | Health, Energy, disaster risk reduction, Agriculture |
Table 3.
Table of continentally averaged absolute bias recorded in 37 CMIP6 GCMs for the 12 extreme indices. A smaller value of bias corresponds to the better performance of GCMs. Here, the colour green and red denote the best (top 25% subset) and worst (bottom 25% subset) performing GCMs.
Table 3.
Table of continentally averaged absolute bias recorded in 37 CMIP6 GCMs for the 12 extreme indices. A smaller value of bias corresponds to the better performance of GCMs. Here, the colour green and red denote the best (top 25% subset) and worst (bottom 25% subset) performing GCMs.
| GCM | R99p | Rx1day | R10mm | CWD | CDD | Txge35 | TR | SU | TXX | TNN | WSDI | CSDI |
---|
1 | ACCESS-CM2 | 2.67 | 0.51 | 1.08 | 0.43 | 3.49 | 1.05 | 3.4 | 2.01 | 1.44 | 2.78 | 7.09 | 0.07 |
2 | ACCESS-ESM1-5 | 7.9 | 0.2 | 0.98 | 0.69 | 4.12 | 0.13 | 3.63 | 1.25 | 0.45 | 2.56 | 5.07 | 0.81 |
3 | AWI-ESM-1-1-LR | 20.46 | 0.93 | 8.22 | 1.08 | 1.34 | 3.13 | 3.45 | 2.69 | 2.42 | 2.79 | 3.95 | 1.34 |
4 | BCC-CSM2-MR | 36.13 | 5.77 | 1.16 | 0.99 | 8.25 | 1.19 | 2.87 | 0.86 | 0.98 | 2.34 | 1.13 | 0.24 |
5 | BCC-ESM1 | 8.26 | 4.74 | 2.67 | 0.58 | 6.12 | 0.19 | 4.62 | 0.68 | 0.1 | 4.2 | 0.55 | 0.06 |
6 | CMCC-CM2-SR5 | 14.7 | 2.05 | 12.03 | 2.48 | 7.73 | 7.37 | 11 | 8.28 | 7.73 | 8.32 | 5.51 | 1.25 |
7 | CNRM-CM6-1-HR | 25.56 | 3.15 | 5.18 | 0.72 | 8.39 | 0.68 | 0.18 | 1.02 | 0.98 | 0.89 | 1.43 | 0.77 |
8 | CNRM-CM6-1 | 21.2 | 1.27 | 1.48 | 0.18 | 3.52 | 1.18 | 1.51 | 1.21 | 1.35 | 1.74 | 3.73 | 0.09 |
9 | CNRM-ESM2-1 | 24.9 | 2.73 | 3.83 | 0.5 | 8.86 | 0.02 | 2.19 | 0.45 | 0.69 | 2.08 | 2.65 | 0.1 |
10 | CanESM5 | 19.85 | 1.64 | 2.29 | 1.12 | 6.03 | 1.38 | 2.97 | 0.39 | 1.34 | 1.71 | 5.88 | 1.42 |
11 | EC-Earth3-Veg-LR | 20.43 | 3.01 | 3.92 | 1.39 | 2.8 | 1.45 | 1.66 | 0.86 | 1.39 | 2.23 | 6.76 | 0.27 |
12 | EC-Earth3-Veg | 22.35 | 3.42 | 1.53 | 0.89 | 4.76 | 0.47 | 2.46 | 0.27 | 0.48 | 2.56 | 9.38 | 0.91 |
13 | EC-Earth3 | 21.91 | 3.89 | 0.4 | 0.53 | 5.37 | 0.06 | 2.6 | 0.72 | 0.08 | 2.82 | 13.37 | 1.19 |
14 | FGOALS-f3-L | 1.35 | 1.73 | 3.83 | 0.62 | 6.99 | 1.83 | 1.33 | 2.55 | 1.95 | 2.64 | 8.03 | 1.33 |
15 | FGOALS-g3 | 2.04 | 1.04 | 0.27 | 0.91 | 8.18 | 0.8 | 2.5 | 0.94 | 1.01 | 3.1 | 2.23 | 0.67 |
16 | GFDL-CM4 | 7.91 | 0.25 | 3.36 | 0.25 | 3.05 | 2.23 | 0.92 | 3.49 | 1.97 | 0.09 | 2.08 | 0.54 |
17 | GFDL-ESM4 | 4.06 | 0.06 | 1.42 | 0.04 | 6.54 | 1.88 | 2.78 | 2.82 | 1.79 | 1.81 | 3.66 | 1.37 |
18 | HadGEM3-GC31-LL | 4.01 | 0.71 | 0.58 | 0.45 | 4.17 | 0.83 | 0.52 | 1.48 | 0.73 | 0.8 | 3.84 | 1.62 |
19 | HadGEM3-GC31-MM | 1.52 | 1.32 | 3.7 | 0.03 | 1.84 | 0.63 | 0.21 | 0.48 | 0.22 | 0.46 | 1.96 | 1.89 |
20 | INM-CM4-8 | 3.62 | 0.94 | 6.63 | 3.69 | 14.56 | 2.24 | 1.77 | 1.64 | 1.84 | 1.67 | 3.87 | 0.19 |
21 | INM-CM5-0 | 27.21 | 2.89 | 3.84 | 2.76 | 13.8 | 2.03 | 0.6 | 2.51 | 2.01 | 0.49 | 1.98 | 0.83 |
22 | IPSL-CM6A-LR | 3.33 | 2.51 | 10.22 | 2.51 | 8.08 | 5.01 | 2.91 | 3.92 | 4.05 | 2.61 | 6.94 | 1.28 |
23 | KACE-1-0-G | 14.99 | 3.14 | 5.43 | 0.14 | 0.33 | 0.66 | 0.32 | 0.45 | 0.1 | 0.87 | 3.09 | 1.67 |
24 | KIOST-ESM | 2.29 | 2.02 | 3.61 | 1.15 | 1.62 | 8 | 3.43 | 3.97 | 8.54 | 2.56 | 0.4 | 0.59 |
25 | MIR°C-ES2L | 4.29 | 0.08 | 5.11 | 2.2 | 9.67 | 1.77 | 4.58 | 0.72 | 1.01 | 4.72 | 3.93 | 1.12 |
26 | MIR°C6 | 13.48 | 5.35 | 7.35 | 1.25 | 10.31 | 4.88 | 4.56 | 1.75 | 4.75 | 4.51 | 1.86 | 0.34 |
27 | MPI-ESM-1-2-HAM | 28.14 | 2.67 | 7.53 | 0.55 | 4.5 | 2.57 | 3.46 | 1.8 | 2.11 | 2.93 | 1.45 | 1.13 |
28 | MPI-ESM1-2-HR | 40.21 | 7.26 | 7.86 | 1.02 | 30.31 | 1.09 | 4.98 | 0.03 | 0.3 | 3.95 | 2.42 | 0.51 |
29 | MPI-ESM1-2-LR | 33.52 | 5.83 | 2.56 | 0.51 | 19.27 | 0.45 | 4.41 | 0.76 | 0.39 | 3.1 | 4.25 | 0.01 |
30 | MRI-ESM2-0 | 19.27 | 3.16 | 3.82 | 0.09 | 0.44 | 1.99 | 3.59 | 0.08 | 1.81 | 2.09 | 0.51 | 1.37 |
31 | NESM3 | 19.6 | 1.05 | 14.22 | 1.04 | 5.21 | 6.13 | 9.27 | 7.16 | 6.61 | 7.05 | 5.01 | 0.27 |
32 | NorCPM1 | 3.92 | 1.17 | 12.52 | 3.19 | 10.02 | 6.07 | 0.56 | 5 | 5.11 | 1.93 | 2.57 | 1.96 |
33 | NorESM2-LM | 5.81 | 1.79 | 6.31 | 1.34 | 6.61 | 2.81 | 4.65 | 2.79 | 2.53 | 4.43 | 1.45 | 0.71 |
34 | NorESM2-MM | 8.97 | 4.47 | 8.02 | 1.16 | 7.79 | 3.09 | 3.41 | 2.98 | 2.48 | 3.54 | 0.11 | 0.39 |
35 | SAM0-UNICON | 14.16 | 1.66 | 7.89 | 1.62 | 2.66 | 2.58 | 1.92 | 1.64 | 1.81 | 2.55 | 6 | 1.39 |
36 | TaiESM1 | 0.44 | 1.32 | 7.09 | 1.62 | 6.41 | 7.1 | 11.14 | 8.52 | 7.34 | 8.35 | 3.9 | 0.95 |
37 | UKESM1-0-LL | 4.06 | 1.16 | 2.68 | 0.04 | 1.31 | 0.88 | 0.71 | 1.7 | 0.89 | 0.26 | 7.11 | 1.01 |
Table 4.
Table of continentally averaged root mean square error (RMSE) recorded in 37 CMIP6 GCMs for the 12 extreme indices. A smaller value of RMSE corresponds to the better performance of GCMs. Here, the colour green and red denote the best (top 25% subset) and worst (bottom 25% subset) performing GCMs.
Table 4.
Table of continentally averaged root mean square error (RMSE) recorded in 37 CMIP6 GCMs for the 12 extreme indices. A smaller value of RMSE corresponds to the better performance of GCMs. Here, the colour green and red denote the best (top 25% subset) and worst (bottom 25% subset) performing GCMs.
| GCM | R99p | Rx1day | R10mm | CWD | CDD | Txge35 | TR | SU | TXX | TNN | WSDI | CSDI |
---|
0 | ACCESS-CM2 | 74.87 | 5.71 | 0.65 | 1.05 | 7.5 | 2.25 | 3.47 | 2.3 | 1.81 | 2.79 | 18.7 | 4.66 |
1 | ACCESS-ESM1-5 | 73.24 | 5.46 | 0.58 | 1.08 | 7.84 | 1.98 | 3.67 | 1.78 | 1.28 | 2.58 | 15.37 | 4.12 |
2 | AWI-ESM-1-1-LR | 59.12 | 5.32 | 0.97 | 1.54 | 9.38 | 3.49 | 3.59 | 2.96 | 2.58 | 2.81 | 17.41 | 5.37 |
3 | BCC-CSM2-MR | 92.9 | 8.11 | 0.62 | 1.28 | 9.56 | 1.93 | 3.1 | 1.44 | 1.45 | 2.48 | 11.12 | 4.15 |
4 | BCC-ESM1 | 77.92 | 7.66 | 0.72 | 1.22 | 8.9 | 1.88 | 4.72 | 1.56 | 1.44 | 4.23 | 12.65 | 4.5 |
5 | CMCC-CM2-SR5 | 76 | 5.62 | 1.15 | 2.62 | 9.19 | 7.37 | 11 | 8.28 | 7.73 | 8.32 | 16.22 | 3.76 |
6 | CNRM-CM6-1-HR | 57.55 | 5.14 | 0.66 | 1.03 | 11.53 | 1.82 | 1.83 | 1.61 | 1.24 | 1.33 | 11.96 | 4.08 |
7 | CNRM-CM6-1 | 60.66 | 5.14 | 0.65 | 0.93 | 9.43 | 2.14 | 2.29 | 1.9 | 1.6 | 1.9 | 14.28 | 4.89 |
8 | CNRM-ESM2-1 | 60.04 | 5.42 | 0.66 | 0.98 | 13.31 | 1.76 | 2.7 | 1.66 | 1.2 | 2.2 | 12.85 | 4.52 |
9 | CanESM5 | 82.74 | 6.4 | 0.7 | 1.5 | 9.15 | 2.66 | 3.12 | 1.79 | 2.48 | 2.25 | 16.77 | 3.76 |
10 | EC-Earth3-Veg-LR | 58.24 | 5.11 | 0.75 | 1.71 | 8.47 | 2.67 | 2.21 | 2.09 | 1.9 | 2.38 | 17.32 | 4.1 |
11 | EC-Earth3-Veg | 58.84 | 5.29 | 0.66 | 1.37 | 9.79 | 2.52 | 2.71 | 1.71 | 1.43 | 2.61 | 18.36 | 3.94 |
12 | EC-Earth3 | 57.8 | 5.31 | 0.59 | 1.13 | 9.66 | 2.39 | 2.85 | 1.61 | 1.24 | 2.85 | 20.48 | 3.61 |
13 | FGOALS-f3-L | 73.3 | 6.41 | 0.64 | 1.05 | 11.76 | 2.31 | 1.86 | 2.76 | 2.05 | 2.68 | 17.66 | 3.56 |
14 | FGOALS-g3 | 68.62 | 5.37 | 0.61 | 1.29 | 9.52 | 1.91 | 2.73 | 1.59 | 1.48 | 3.12 | 12.86 | 3.74 |
15 | GFDL-CM4 | 65.14 | 5.22 | 0.56 | 0.81 | 8.64 | 2.61 | 1.6 | 3.52 | 2.06 | 1.11 | 13.51 | 4.21 |
16 | GFDL-ESM4 | 68.16 | 5.48 | 0.56 | 0.9 | 11.76 | 2.48 | 2.93 | 3.01 | 1.96 | 1.92 | 14.85 | 3.46 |
17 | HadGEM3-GC31-LL | 71.27 | 5.25 | 0.59 | 1.01 | 7.45 | 2.11 | 1.73 | 1.82 | 1.33 | 1.67 | 14.47 | 3.63 |
18 | HadGEM3-GC31-MM | 68.15 | 5.03 | 0.55 | 0.75 | 6.68 | 1.72 | 1.48 | 1.19 | 0.98 | 1.29 | 12.15 | 3.33 |
19 | INM-CM4-8 | 70.85 | 5.35 | 0.8 | 3.72 | 14.6 | 2.92 | 2.23 | 2.09 | 2.06 | 2.24 | 14.42 | 4.08 |
20 | INM-CM5-0 | 88.48 | 6.26 | 0.71 | 2.81 | 13.86 | 2.86 | 1.91 | 2.73 | 2.17 | 1.92 | 14.45 | 4.8 |
21 | IPSL-CM6A-LR | 69.71 | 6.03 | 1.09 | 2.72 | 9.96 | 5.06 | 3.02 | 3.95 | 4.06 | 2.64 | 16.42 | 3.77 |
22 | KACE-1-0-G | 61.6 | 5.36 | 0.64 | 0.89 | 8.05 | 1.82 | 1.77 | 1.44 | 1.26 | 1.68 | 13.22 | 3.34 |
23 | KIOST-ESM | 69.4 | 5.47 | 0.62 | 1.55 | 7.83 | 8.18 | 3.58 | 4.38 | 8.59 | 2.71 | 15.19 | 4.85 |
24 | MIR°C-ES2L | 61.93 | 4.74 | 0.74 | 2.3 | 10.43 | 2.49 | 4.65 | 1.51 | 1.77 | 4.72 | 14.34 | 5.14 |
25 | MIR°C6 | 75.53 | 7.43 | 0.85 | 1.47 | 11.06 | 5.04 | 4.58 | 2.15 | 4.85 | 4.51 | 15.75 | 4.24 |
26 | MPI-ESM-1-2-HAM | 54.65 | 5.08 | 1.02 | 1.38 | 10.63 | 2.99 | 3.5 | 2.18 | 2.29 | 2.93 | 12.41 | 5.27 |
27 | MPI-ESM1-2-HR | 53.7 | 7.71 | 0.78 | 1.3 | 32.47 | 2.04 | 4.99 | 1.26 | 1.11 | 3.95 | 14.84 | 4.07 |
28 | MPI-ESM1-2-LR | 55.17 | 6.69 | 0.63 | 1.09 | 21.65 | 1.98 | 4.42 | 1.56 | 1.31 | 3.11 | 15.38 | 4.29 |
29 | MRI-ESM2-0 | 62.8 | 5.69 | 0.63 | 0.86 | 8.42 | 2.51 | 3.62 | 1.49 | 2.23 | 2.21 | 13 | 3.79 |
30 | NESM3 | 59.32 | 4.55 | 1.39 | 1.48 | 9.77 | 6.14 | 9.27 | 7.18 | 6.61 | 7.05 | 16.01 | 4.46 |
31 | NorCPM1 | 66.8 | 5.41 | 1.27 | 3.28 | 10.86 | 6.07 | 2.02 | 5.03 | 5.12 | 2.13 | 14.84 | 5.57 |
32 | NorESM2-LM | 64.93 | 5.99 | 0.9 | 1.64 | 9.31 | 3.09 | 4.66 | 2.87 | 2.62 | 4.43 | 13.61 | 5.02 |
33 | NorESM2-MM | 72.54 | 6.86 | 0.88 | 1.38 | 9.36 | 3.21 | 3.5 | 3.06 | 2.52 | 3.54 | 12.47 | 4.43 |
34 | SAM0-UNICON | 79.68 | 6.16 | 1 | 1.96 | 8.65 | 3.14 | 2.25 | 2.08 | 2 | 2.63 | 15.61 | 5.53 |
35 | TaiESM1 | 67.91 | 5.45 | 0.87 | 1.86 | 9.25 | 7.1 | 11.14 | 8.52 | 7.34 | 8.35 | 16.41 | 4.04 |
36 | UKESM1-0-LL | 71.46 | 5.09 | 0.57 | 0.85 | 7.25 | 1.91 | 1.71 | 1.92 | 1.35 | 1.55 | 15.48 | 3.88 |
Table 5.
Table of pattern correlation (PCorr) recorded in 37 CMIP6 GCMs for the 12 extreme indices. A larger value of PCorr corresponds to the better performance of GCMs. Here, the colour green and red denote the best (top 25% subset) and worst (bottom 25% subset) performing GCMs.
Table 5.
Table of pattern correlation (PCorr) recorded in 37 CMIP6 GCMs for the 12 extreme indices. A larger value of PCorr corresponds to the better performance of GCMs. Here, the colour green and red denote the best (top 25% subset) and worst (bottom 25% subset) performing GCMs.
| GCM | R99p | Rx1day | R10mm | CWD | CDD | Txge35 | TR | SU | TXX | TNN | WSDI | CSDI |
---|
0 | ACCESS-CM2 | 0.62 | 0.79 | 0.9 | 0.87 | 0.81 | 0.96 | 0.98 | 0.98 | 0.96 | 0.97 | 0.67 | 0.41 |
1 | ACCESS-ESM1-5 | 0.64 | 0.73 | 0.89 | 0.9 | 0.75 | 0.95 | 0.97 | 0.98 | 0.94 | 0.95 | 0.76 | 0.61 |
2 | AWI-ESM-1-1-LR | 0.43 | 0.69 | 0.85 | 0.83 | 0.65 | 0.88 | 0.98 | 0.98 | 0.96 | 0.95 | 0.64 | 0.53 |
3 | BCC-CSM2-MR | 0.8 | 0.77 | 0.83 | 0.87 | 0.62 | 0.97 | 0.96 | 0.99 | 0.95 | 0.94 | 0.68 | 0.32 |
4 | BCC-ESM1 | 0.54 | 0.57 | 0.8 | 0.68 | 0.52 | 0.94 | 0.9 | 0.98 | 0.89 | 0.85 | 0.64 | 0.39 |
5 | CMCC-CM2-SR5 | 0.62 | 0.8 | 0.83 | 0.77 | 0.78 | 0.37 | 0.96 | 0.95 | 0.95 | 0.98 | 0.67 | 0.32 |
6 | CNRM-CM6-1-HR | 0.64 | 0.86 | 0.72 | 0.79 | 0.83 | 0.97 | 0.94 | 0.98 | 0.97 | 0.96 | 0.77 | 0.39 |
7 | CNRM-CM6-1 | 0.46 | 0.76 | 0.77 | 0.83 | 0.78 | 0.97 | 0.96 | 0.97 | 0.97 | 0.96 | 0.71 | 0.48 |
8 | CNRM-ESM2-1 | 0.59 | 0.8 | 0.8 | 0.83 | 0.81 | 0.97 | 0.95 | 0.98 | 0.97 | 0.95 | 0.66 | 0.41 |
9 | CanESM5 | 0.65 | 0.69 | 0.86 | 0.8 | 0.62 | 0.91 | 0.94 | 0.98 | 0.92 | 0.89 | 0.65 | 0.36 |
10 | EC-Earth3-Veg-LR | 0.63 | 0.89 | 0.92 | 0.91 | 0.84 | 0.84 | 0.97 | 0.97 | 0.92 | 0.95 | 0.73 | 0.22 |
11 | EC-Earth3-Veg | 0.66 | 0.9 | 0.9 | 0.91 | 0.86 | 0.85 | 0.97 | 0.98 | 0.92 | 0.96 | 0.74 | 0.31 |
12 | EC-Earth3 | 0.73 | 0.91 | 0.92 | 0.93 | 0.85 | 0.87 | 0.97 | 0.98 | 0.93 | 0.96 | 0.68 | 0.13 |
13 | FGOALS-f3-L | 0.67 | 0.87 | 0.87 | 0.76 | 0.8 | 0.95 | 0.98 | 0.96 | 0.95 | 0.96 | 0.73 | 0.44 |
14 | FGOALS-g3 | 0.72 | 0.74 | 0.86 | 0.81 | 0.69 | 0.97 | 0.97 | 0.98 | 0.94 | 0.93 | 0.73 | 0.54 |
15 | GFDL-CM4 | 0.73 | 0.85 | 0.94 | 0.95 | 0.81 | 0.95 | 0.99 | 0.98 | 0.98 | 0.98 | 0.58 | 0.23 |
16 | GFDL-ESM4 | 0.68 | 0.86 | 0.94 | 0.95 | 0.81 | 0.92 | 0.98 | 0.96 | 0.97 | 0.96 | 0.72 | 0.48 |
17 | HadGEM3-GC31-LL | 0.73 | 0.84 | 0.92 | 0.89 | 0.77 | 0.94 | 0.96 | 0.99 | 0.95 | 0.9 | 0.71 | 0.52 |
18 | HadGEM3-GC31-MM | 0.76 | 0.88 | 0.96 | 0.96 | 0.81 | 0.98 | 0.97 | 0.99 | 0.98 | 0.94 | 0.7 | 0.38 |
19 | INM-CM4-8 | 0.36 | 0.68 | 0.92 | 0.81 | 0.72 | 0.78 | 0.95 | 0.98 | 0.94 | 0.87 | 0.74 | 0.43 |
20 | INM-CM5-0 | 0.59 | 0.8 | 0.9 | 0.82 | 0.66 | 0.81 | 0.94 | 0.97 | 0.95 | 0.87 | 0.75 | 0.45 |
21 | IPSL-CM6A-LR | 0.63 | 0.74 | 0.77 | 0.8 | 0.59 | 0.65 | 0.97 | 0.97 | 0.98 | 0.95 | 0.7 | 0.27 |
22 | KACE-1-0-G | 0.72 | 0.82 | 0.91 | 0.87 | 0.73 | 0.95 | 0.95 | 0.98 | 0.91 | 0.91 | 0.7 | 0.32 |
23 | KIOST-ESM | 0.7 | 0.74 | 0.79 | 0.79 | 0.72 | 0.69 | 0.96 | 0.91 | 0.72 | 0.93 | 0.57 | 0.5 |
24 | MIR°C-ES2L | 0.8 | 0.8 | 0.87 | 0.81 | 0.65 | 0.92 | 0.9 | 0.97 | 0.87 | 0.89 | 0.62 | 0.48 |
25 | MIR°C6 | 0.73 | 0.73 | 0.9 | 0.86 | 0.6 | 0.91 | 0.96 | 0.98 | 0.92 | 0.95 | 0.59 | 0.49 |
26 | MPI-ESM-1-2-HAM | 0.64 | 0.73 | 0.71 | 0.69 | 0.59 | 0.92 | 0.97 | 0.99 | 0.95 | 0.96 | 0.76 | 0.42 |
27 | MPI-ESM1-2-HR | 0.49 | 0.72 | 0.88 | 0.86 | 0.78 | 0.98 | 0.98 | 0.99 | 0.98 | 0.97 | 0.65 | 0.55 |
28 | MPI-ESM1-2-LR | 0.69 | 0.78 | 0.86 | 0.88 | 0.75 | 0.95 | 0.98 | 0.99 | 0.94 | 0.96 | 0.72 | 0.46 |
29 | MRI-ESM2-0 | 0.62 | 0.8 | 0.92 | 0.89 | 0.69 | 0.97 | 0.99 | 0.99 | 0.97 | 0.96 | 0.66 | 0.53 |
30 | NESM3 | 0.65 | 0.76 | 0.76 | 0.8 | 0.77 | 0.44 | 0.94 | 0.92 | 0.89 | 0.96 | 0.74 | 0.19 |
31 | NorCPM1 | 0.6 | 0.61 | 0.73 | 0.73 | 0.71 | 0.61 | 0.95 | 0.95 | 0.9 | 0.93 | 0.49 | 0.4 |
32 | NorESM2-LM | 0.75 | 0.68 | 0.79 | 0.8 | 0.59 | 0.9 | 0.97 | 0.98 | 0.93 | 0.95 | 0.6 | 0.61 |
33 | NorESM2-MM | 0.71 | 0.74 | 0.86 | 0.89 | 0.7 | 0.92 | 0.98 | 0.99 | 0.96 | 0.97 | 0.68 | 0.58 |
34 | SAM0-UNICON | 0.63 | 0.69 | 0.8 | 0.85 | 0.65 | 0.81 | 0.98 | 0.98 | 0.94 | 0.96 | 0.66 | 0.51 |
35 | TaiESM1 | 0.77 | 0.9 | 0.9 | 0.87 | 0.82 | 0.51 | 0.96 | 0.93 | 0.96 | 0.97 | 0.67 | 0.27 |
36 | UKESM1-0-LL | 0.66 | 0.86 | 0.92 | 0.91 | 0.76 | 0.97 | 0.96 | 0.99 | 0.95 | 0.91 | 0.64 | 0.37 |
Table 6.
Table of interannual variability score (IVS) recorded in 37 CMIP6 GCMs for the 12 extreme indices. A smaller value of IVS corresponds to the better performance of GCMs. Here, the colour green and red denote the best (top 25% subset) and worst (bottom 25% subset) performing GCMs.
Table 6.
Table of interannual variability score (IVS) recorded in 37 CMIP6 GCMs for the 12 extreme indices. A smaller value of IVS corresponds to the better performance of GCMs. Here, the colour green and red denote the best (top 25% subset) and worst (bottom 25% subset) performing GCMs.
| GCM | R99p | Rx1day | R10mm | CWD | CDD | Txge35 | TR | SU | TXX | TNN | WSDI | CSDI |
---|
0 | ACCESS-CM2 | 7.41 | 0.26 | 0.05 | 0.07 | 1.55 | 0.09 | 0.2 | 0.09 | 0.06 | 0.07 | 4.56 | 0.3 |
1 | ACCESS-ESM1-5 | 5.57 | 0.15 | 0.04 | 0.04 | 1.59 | 0.36 | 0.14 | 0.23 | 0.03 | 0.14 | 2.2 | 0.49 |
2 | AWI-ESM-1-1-LR | 6.15 | 0.06 | 0.17 | 0.28 | 0.85 | 0.15 | 0.32 | 0.65 | 0.43 | 0.1 | 5.46 | 0.6 |
3 | BCC-CSM2-MR | 6.53 | 0.18 | 0.07 | 0.01 | 3.01 | 0.55 | 0.04 | 0.08 | 0.14 | 0.05 | 3.06 | 0.46 |
4 | BCC-ESM1 | 1.44 | 0.4 | 0.03 | 0.06 | 2.39 | 0.42 | 0.03 | 0.17 | 0.09 | 0.07 | 1.17 | 0.19 |
5 | CMCC-CM2-SR5 | 12.05 | 1.12 | 0.28 | 0.51 | 0.38 | 0.73 | 0.4 | 0.29 | 0.14 | 0.28 | 7.59 | 0.33 |
6 | CNRM-CM6-1-HR | 6.13 | 0.65 | 0.11 | 0.12 | 1.06 | 0.03 | 0.22 | 0.08 | 0.14 | 0 | 0.24 | 0.37 |
7 | CNRM-CM6-1 | 5.41 | 0.45 | 0.05 | 0.06 | 0.04 | 0.26 | 0.22 | 0.13 | 0.08 | 0.11 | 0.07 | 0.55 |
8 | CNRM-ESM2-1 | 4.2 | 0.37 | 0.04 | 0.04 | 0.77 | 0.21 | 0.1 | 0.24 | 0.05 | 0.03 | 0.18 | 0.23 |
9 | CanESM5 | 6.31 | 0.19 | 0.06 | 0.12 | 1.75 | 0.13 | 0.03 | 0.26 | 0.14 | 0.05 | 3.35 | 0.96 |
10 | EC-Earth3-Veg-LR | 3.38 | 0.25 | 0.13 | 0.48 | 0.03 | 0.1 | 0.17 | 0.68 | 0.41 | 0.03 | 7.03 | 0.06 |
11 | EC-Earth3-Veg | 5.85 | 0.56 | 0.01 | 0.22 | 0.01 | 0.14 | 0.15 | 0.26 | 0.07 | 0.03 | 4.94 | 0.6 |
12 | EC-Earth3 | 4.99 | 0.7 | 0.01 | 0.05 | 0.92 | 0.11 | 0.15 | 0.08 | 0.09 | 0.05 | 6.74 | 0.64 |
13 | FGOALS-f3-L | 1.64 | 0.57 | 0.07 | 0.1 | 0.06 | 0.35 | 0.17 | 0.21 | 0.1 | 0 | 3.71 | 0.41 |
14 | FGOALS-g3 | 4.02 | 0.06 | 0.02 | 0.11 | 2.21 | 0.32 | 0.02 | 0.02 | 0.14 | 0.11 | 1.55 | 0.16 |
15 | GFDL-CM4 | 1.18 | 0.08 | 0.05 | 0.03 | 0.87 | 0.3 | 0.12 | 0.1 | 0.05 | 0.18 | 1.09 | 0.24 |
16 | GFDL-ESM4 | 0.61 | 0.3 | 0.01 | 0.13 | 1.72 | 0.3 | 0.21 | 0.37 | 0.06 | 0.04 | 2.8 | 0.82 |
17 | HadGEM3-GC31-LL | 0.3 | 0.27 | 0.05 | 0.01 | 1.9 | 0.37 | 0.17 | 0.03 | 0.11 | 0.14 | 1.53 | 1.2 |
18 | HadGEM3-GC31-MM | 0.8 | 0.46 | 0.11 | 0.1 | 2.02 | 0.46 | 0.11 | 0.06 | 0.09 | 0.07 | 0.66 | 1.22 |
19 | INM-CM4-8 | 2.1 | 0.47 | 0.07 | 0.21 | 3.51 | 0.22 | 0.05 | 0.08 | 0.02 | 0.14 | 3.26 | 0 |
20 | INM-CM5-0 | 3.58 | 0.33 | 0.02 | 0.06 | 3.18 | 0.25 | 0.02 | 0.02 | 0.05 | 0.12 | 0.58 | 0.22 |
21 | IPSL-CM6A-LR | 4.62 | 0.42 | 0.17 | 0.48 | 2.36 | 0.33 | 0 | 0.09 | 0.05 | 0 | 3.46 | 0.73 |
22 | KACE-1-0-G | 0.07 | 0.39 | 0.08 | 0.03 | 0.88 | 0.34 | 0.04 | 0.06 | 0.16 | 0.01 | 0.36 | 1 |
23 | KIOST-ESM | 4.8 | 0.67 | 0.13 | 0.05 | 2.45 | 0.24 | 0.55 | 0.33 | 0.55 | 0.14 | 0.37 | 0.91 |
24 | MIR°C-ES2L | 1.51 | 0.38 | 0.03 | 0.24 | 2.38 | 0.16 | 0.03 | 0.06 | 0.06 | 0.04 | 1.19 | 0.76 |
25 | MIR°C6 | 2.88 | 0.82 | 0.12 | 0.1 | 2.44 | 0.31 | 0.43 | 0.26 | 0.69 | 0.11 | 3.88 | 0.14 |
26 | MPI-ESM-1-2-HAM | 5.57 | 0.58 | 0 | 0.02 | 0.15 | 0.4 | 0.08 | 0.29 | 0.05 | 0.01 | 0.23 | 0.65 |
27 | MPI-ESM1-2-HR | 10.16 | 0.6 | 0.03 | 0.04 | 7.84 | 0.06 | 0.01 | 0.12 | 0.06 | 0.04 | 1.03 | 0.26 |
28 | MPI-ESM1-2-LR | 9.41 | 0.43 | 0.02 | 0.05 | 3.69 | 0.02 | 0.25 | 0.26 | 0.14 | 0.11 | 2.98 | 0.41 |
29 | MRI-ESM2-0 | 1.81 | 0.26 | 0.03 | 0.03 | 0.69 | 0.2 | 0.12 | 0.23 | 0.01 | 0.11 | 1.95 | 0.18 |
30 | NESM3 | 3.92 | 0.86 | 0.07 | 0.04 | 1.75 | 0.59 | 0.02 | 0.03 | 0.07 | 0.02 | 2.6 | 0.31 |
31 | NorCPM1 | 0.09 | 0.34 | 0.14 | 0.37 | 2.29 | 0.39 | 0.06 | 0.42 | 0.15 | 0.02 | 1.97 | 0.9 |
32 | NorESM2-LM | 0.4 | 0.59 | 0.15 | 0.17 | 1.81 | 0.35 | 0.07 | 0.2 | 0.19 | 0.09 | 0.18 | 0.42 |
33 | NorESM2-MM | 2.06 | 1.35 | 0.24 | 0.28 | 0.79 | 0.04 | 0.12 | 0.1 | 0.01 | 0.07 | 0.07 | 0.13 |
34 | SAM0-UNICON | 6.38 | 0.5 | 0.23 | 0.38 | 0.07 | 0.06 | 0.19 | 0.2 | 0.09 | 0.07 | 1.39 | 1.36 |
35 | TaiESM1 | 5.02 | 0.66 | 0.16 | 0.35 | 0.33 | 0.79 | 0.25 | 0.13 | 0.08 | 0.17 | 3.3 | 0.43 |
36 | UKESM1-0-LL | 2.85 | 0.6 | 0.11 | 0.11 | 1.82 | 0.3 | 0.16 | 0.14 | 0.03 | 0.11 | 0.88 | 0.47 |