1. Extreme precipitation changes for the different PDRMIP climate drivers
Camilla W. Stjern, Jana Sillmann, Gunnar Myhre, and contributing PDRMIP members

2. Extreme precipitation changes more than average precipitation
Extreme precipitation increases more than mean precipitation with global warming
Change in average precipitation is highly dependent on forcing mechanism (Andrews et al.; 2010 Fläschner et al., 2016)
Some studies indicate that change in extreme precipitation (rx1day) is independent on forcing mechanism (Pendergrass et al., 2015)
Lin et al. (2015) and Sillmann et al. (2017) find indications of driver dependency for more moderate extreme indices (rx5day)
More heavy precipitation events and fewer light precipitation events?
AR5, fig 7.21: Estimated increase (per degree warming) in average and extreme (99.9th percentile) precipitation with increase CO2.
Fischer and Knutti (2016), fig 1: Conceptual illustration of heavy rainfall intensification.

3. PDRMIP changes in mean and extreme precipitation
In general, extreme precipitation changes more than the mean precipitation for all drivers
The 99.99th percentile changes more than rx1day
Clear differences between forcing mechanisms for change in mean prec., but less so for extremes
BCx10: - largest model spread - significantly different from other drivers - less positive change in extremes, and a drying in the mean
Equivalent to the 99.7th percentile. Occurs (per definition) once a year.
Occurs about once every 30 years.

4. PDRMIP changes in mean and extreme precipitation
In general, extreme precipitation changes more than the mean precipitation for all drivers
The 99.99th percentile changes more than rx1day
Clear differences between forcing mechanisms for change in mean prec., but less so for extremes
BCx10: - largest model spread - significantly different from other drivers - less positive change in extremes, and a drying in the mean
Equivalent to the 99.7th percentile. Occurs (per definition) once a year.
Occurs about once every 30 years.

5. Change in consecutive dry days (CDD)
Consecutive dry days is the greatest number of consecutive days per time period with daily precipitation amount below 1 mm
(ΔT is negative for SO4x5, so all drivers cause an increase in CDD globally)
The CDD change per K change in global temp. is largest in BCx10, but so is the spread
BC contributes to a large spread in both dry and wet extremes, and probably contributes to the uncertainty in estimates of changes in extremes in general

6. Change in consecutive dry days (CDD)
Consecutive dry days is the largest number of consecutive days per time period with daily precipitation amount below 1 mm
(ΔT is negative for SO4x5, so all drivers cause an increase in CDD globally)
The CDD change per K change in global temp. is largest in BCx10, but so is the spread
BC contributes to a large spread in both dry and wet extremes, and probably contributes to the uncertainty in estimates of changes in extremes in general
Mean ΔCDD/ΔT increases over land for all drivers, but over extratropical land only BCx10 shows an increase

7. Geographical pattern of model mean ΔCDD/ ΔT change
CO2x CH4x SOL
Geographical pattern similar between drivers
BC also has a similar pattern, but much stronger magnitude of change
BCx10 shows a strong increase in CDD in the extratropics. Due to expansion of the Hadley cell??
BCx SO4x5
25.0
12.5
-12.5
-25.0

8. Change in frequency of occurence
For BCx10: an increase in the frequency of dry days
All cases: reduction in the frequency of all precipitation events with prec. lower than the 90th percentile
Outlier model values are included in «median [minmodel to maxmodel]» text to the right, but are not plotted.
Outliers are defined as values larger/smaller than (1./3. quantile +/- 1.5*IQR).

9. Change in frequency of occurence
The more extreme the precipitation, the larger the relative increase in how often it occurs
For the «less extreme extremes», BC is different than the other drivers, but the pattern of change becomes similar to the other drivers for the higher percentiles
Outlier model values are included in «median [minmodel to maxmodel]» text to the right, but are not plotted.
Outliers are defined as values larger/smaller than (1./3. quantile +/- 1.5*IQR).

10. Rapid ajustments versus feedback responses
Total change: All drivers cause a decrease in precipitation in the lower precip. bins. For all cases but BCx10, this decrease seems to originate from the tropics
[EXAMPLE]
Average pr
Median pr
rx1day
50th
90th
99th
99.9th
99.99th
99.999th
CanESM2, BASE
2.74
1.15
36.69
7.2
20.22
49.49
72.13
84.23

11. Rapid ajustments versus feedback responses
Total change: All drivers cause a decrease in precipitation in the lower precip. bins. For all cases but BCx10, this decrease seems to originate from the extratropics
Slow change: For all drivers but BC, it seems that most of the prec. change in all prec. bins originate from the feedback response
Average pr
Median pr
rx1day
50th
90th
99th
99.9th
99.99th
99.999th
CanESM, BASE
2.74
1.15
36.69
7.2
20.22
49.49
72.13
84.23

12. Rapid ajustments versus feedback responses
Total change: All drivers cause a decrease in precipitation in the lower precip. bins. For all cases but BCx10, this decrease seems to originate from the tropics
Slow change: For all drivers but BC, it seems that most of the prec. change in all prec. bins originate from the feedback response
Fast change: All drivers have mostly negative rapid adjustment for global precipitation intensities.
The rapid precipitation adjustment for BCx10 is negative for all precipitation intensities, in global average. However, for extratropical land, it becomes similar to other drivers for the highest intensities
Average pr
Median pr
rx1day
50th
90th
99th
99.9th
99.99th
99.999th
CanESM, BASE
2.74
1.15
36.69
7.2
20.22
49.49
72.13
84.23

13. Camilla W. Stjern

14. CanESM2, BASE percentiles
Example of spatial variation in mm/day-values of precipitation percentiles
CanESM2, BASE percentiles
[mm/day]
Note different colorbar limits for each plot!

15. Example (CanESM2) of spatial change in frequency of occurence
BASE globally averaged frequency of occurence [%]:
0mm:
0mm-50th:
50th-75th:
75th-90th:
90th-99th:
99th-99.9th:
99.9th-99.99th:
99.99th th:
99.999th --> :

16. Conceptual illustration of shift in rain distribution (example: CanESM2)
Average pr
Median pr
rx1day
50th
90th
99th
99.9th
99.99th
99.999th
CanESM, BASE
2.74
1.15
36.69
7.2
20.22
49.49
72.13
84.23
Frequency of occurence [%]
Decrease in median precipitation
Increase in mean precipitation
Sligthly larger increase in extreme precipitation
BASE
CO2x2
Median:
Δ =
Mean:
Δ =
Δ 99.99th
percentile =
Median: 1.15 / 1.09
Δ = %
Mean: 2.74 / 2.83
Δ = %
99.9th = / 56.34
Δ = %
Precipitation [mm/day]

17. Conceptual illustration of shift in rain distribution (example: CanESM2)
Frequency of occurence [%]
Reduction in the frequency of occurence of baseline (BASE) median precipitation
Increase in the frequency of occurence of baseline mean precipitation
Significantly larger increase in the frequency of occurence of extreme precipitation
BASE
CO2x2
Median: % / %
Δ = %
Mean: % / %
Δ = %
99.99th: 0.10 % /0.13 %
Δ = %
Median: 1.15 / 1.09
Δ = %
Mean: 2.74 / 2.83
Δ = %
99.9th: / 56.34
Δ = %
Precipitation [mm/day]

18. MODEL MEAN CHANGE VALUESpr
wet days (p > 1 mm/d)
dry days ( p < 1 mm/d)
CDD
rx1day
50th
90th
99th
99.9th
BASE
3.002
42.056
35.850
1.225
8.149
22.026
35.254
CO2x2
0.104
-1.453
1.453
1.001
4.040
-0.021
0.261
1.614
3.980
CH4x3
0.037
-0.098
0.098
0.274
1.211
BCx10
-0.039
-2.117
2.117
1.486
0.550
-0.033
-0.383
0.190
0.970
SO4x5
-0.183
-2.009
2.009
0.806
-3.359
-0.046
-0.349
-1.196
-2.733
SOL
0.171
0.802
-0.802
0.397
4.922

19. Change in percentile values
In principle, this is a more detailed version of the first figure, where rx1day is typically the 99.7th percentile, so slightly below the 99.9th. It shows that although the difference in the increase in %/K, going from one percentile to the next, is not huge, there is a quite consistent increase towards higher percentiles. It also shows that BC is different for the lower percentiles, but becomes like the other drivers for the higher percentiles.
b) Extratropical land
a) Global

20. Prec. above the 99.999th bin occurs 0.002% of the time.
Model-average BASE frequency of occurence (%) of the different precipitation bins:
Prec. above the th bin occurs 0.002% of the time.
(365*100)*(0.002/100)=0.7  these events don’t even occur once every 100 years..
Occur about once every 50 years
BCx10 model-avg freq of occurence for last bin is (BASE=0.002)  250% change

21. Supplementary fig. showing change in percentile values shows the same (relatively low) increase in change towards higher percentiles
Median CO2 change in rx1day (~99.7th percentile): 4.5 %/K Median CO2 change in 99.99th percentile: 7 %/K
Although, as Figure 1 shows, the change in how extreme the extreme precipitation is (the values of rx1day and the percentiles) increases the more extreme the extreme index is, this increase is not very large (from about 4 to about 7 %/K going from rx1day to 99.99th perc.). However, the frequency (so: how often the occur) of the extreme events increase more the more extreme the precipitation, as this figure shows.

22. Global slow and fast changes, individual models
b) Slow
a) Fast

23. Percentile plot, area fraction
Calculate percentile values for BASE for each grid cell
Go through day by day: - for BASE and the five cases, look at global matrix of daily precipitation amounts for that time step - calculate area fraction as:
Total area of the grid cells fulfilling the criteria (e.g. daily prec above the local 75th percentile) divided by the area of the globe
8 precip. exceedance thresholds
The area fractions are then averaged over all time steps (skipping the first 50 years), and the change relative to base is calculated in %
x -1

24. Percentile plot, change in percentile values
Calculate percentile values for BASE and the five cases for each grid cell (skipping the first 50 years)
Globally average the values of each of the percentiles for BASE and the cases
For each case, calculate the percent change in the percentile values between case and BASE
Normalize this change by the globally averaged temperature change for the given model and case
Changes in the high percentiles are small for BCx10 compared to other cases
Crazy spread for BCx10 is due to division by very small temperature changes for CAM5 and SPRINTARS