UNDERSTANDING, DETECTING AND COMPARING EXTREME PRECIPITATION CHANGES OVER MEDITERRANEAN USING...

UNDERSTANDING, DETECTING AND COMPARING EXTREME PRECIPITATION

CHANGES OVER MEDITERRANEAN USING CLIMATE MODELS

Dr. Christina Anagnostopoulou

 Department of Meteorology-Climatology, School of GeologyDepartment of Meteorology-Climatology, School of Geology

Aristotle University of ThessalonikiAristotle University of Thessaloniki

GreeceGreece

• To assess the ability of RCMs datasets to simulate extreme daily

precipitation

• To produce estimates of predicted changes in return levels by

future time periods (2031-2050 and 2081-2100)

• Detection of extreme precipitation assuming that model

predictions are accurate

AimAim

• Data and methods

• Results for selected grid points

• Spatial distribution of the extreme precipitation indices

• Differences of the extreme precipitation indices between

future and reference time period

OutlineOutline

KNMI

DataData

C4IRCMs data for Mediterranean region

Window: 10oW – 35oE

31oN - 45oN

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

0 10 20 30 40 50 60 70 80 90 100

0 20 40 60 80 100 120 140 160 180 200

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

0 10 20 30 40 50 60 70 80 90 100

0 20 40 60 80 100 120 140 160 180 200

Description of RCMs used

• KNMI-RACMO2: Royal Netherlands Meteorological Institute (KNMI, Lenderink et al., 2003; van den Hurk et al., 2006)

• ‘Parent’ ECHAM5

• Time period 1950-2100

• SRES A1B

• Physical parameterizations of ΕCMWF (European Centre for Medium – Range Weather Forecasts) used also for ERA-40 (http://www.ecmwf.int/research/ifsdocs).

• Spatial Resolution 25x25km.

Description of RCMs used

• C4IRCA3 : Community Climate Change Consortium for Ireland (C4I).

• ‘Parent’ ECHAM5

• Time period 1950-2050

• SRES A2

• RCA3 the third version of the Rossby Centre Atmospheric model (Kjellström et al., 2005)

• Spatial Resolution 25x25km.

Methodology

Geveralized Extreme Value DistributionGeveralized Extreme Value Distribution

ξ1

σμz

ξ1expzG μ: location parameter

σ: scale parameter

ξ: shape parameter

Return level

p

ξp

p

logyσμ

y1ξ

σμ

z

ˆˆ

ˆ

ˆ ˆ

for ξ ≠ 0

for ξ = 0

)1log( py p

Estimation for GEV distribution

1

11

11log)11(log),,(

m

im

i zzm

01

iz

),,(,....;,,,...\,, 11 mm Lf

1. Maximum Likelihood Estimation-MLE1. Maximum Likelihood Estimation-MLE

2. Bayesian Method2. Bayesian Method

Methodology

Reference period:1951-2000

• 20year period: 2031-2050

• 20year period: 2081-2100

Indices

• Pm: median Pm(t)=X0.5(t)

• P20 : 20-year return value P20(t)=X0.95(t)

• P100: 100-year return value P100(t)=X0.99(t)

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

0 10 20 30 40 50 60 70 80 90 100

0 20 40 60 80 100 120 140 160 180 200

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

0 10 20 30 40 50 60 70 80 90 100

0 20 40 60 80 100 120 140 160 180 200

Western Western MediterraneanMediterranean

Central Central MediterraneanMediterranean

Eastern Eastern MediterraneanMediterranean

0 50 100 150 200

0.0

00

.01

0.0

20

.03

0.0

40

.05

N = 50 Bandwidth = 4.455

De

nsi

ty

0 50 100 150 200

0.0

00

.01

0.0

20

.03

0.0

40

.05

0 10 20 30 40 50

05

01

00

15

02

00

0 10 20 30 40 50

05

01

00

15

02

00

year

pre

cip

itatio

n

0 10 20 30 40 50

05

01

00

15

02

00

0 10 20 30 40 50

05

01

00

15

02

00

year

pre

cip

itatio

n

0 50 100 150 200

0.0

00

.01

0.0

20

.03

0.0

40

.05

N = 50 Bandwidth = 7.187

De

nsi

ty

0 50 100 150 200

0.0

00

.01

0.0

20

.03

0.0

40

.05

0 10 20 30 40 50

05

01

00

15

02

00

0 10 20 30 40 50

05

01

00

15

02

00

year

pre

cip

itatio

n

0 50 100 150 200

0.0

00

.01

0.0

20

.03

0.0

40

.05

N = 50 Bandwidth = 4.438

De

nsi

ty

0 50 100 150 200

0.0

00

.01

0.0

20

.03

0.0

40

.05

Maximum Likelihood Estimation-MLE Maximum Likelihood Estimation-MLE

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Probability Plot

Empirical

Mod

el

30 40 50 60

3040

5060

70

Quantile Plot

Model

Em

piric

al

1e-01 1e+00 1e+01 1e+02 1e+03

2030

4050

6070

Return Period

Ret

urn

Leve

l

Return Level Plot Density Plot

z

f(z)

20 30 40 50 60 70 80

0.00

00.

010

0.02

00.

030

KNMI C4I0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Probability Plot

Empirical

Mod

el

20 30 40 50 60 70 80

3040

5060

7080

90

Quantile Plot

Model

Em

piric

al

1e-01 1e+00 1e+01 1e+02 1e+03

2040

6080

100

120

140

Return Period

Ret

urn

Leve

l

Return Level Plot Density Plot

z

f(z)

20 40 60 80 100

0.00

0.01

0.02

0.03

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Probability Plot

Empirical

Mod

el

20 40 60 80 100

2040

6080

100

120

Quantile Plot

Model

Em

piric

al

1e-01 1e+00 1e+01 1e+02 1e+03

5010

015

020

0

Return Period

Ret

urn

Leve

l

Return Level Plot Density Plot

z

f(z)

0 20 40 60 80 100 120 140

0.00

00.

010

0.02

00.

030

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Probability Plot

Empirical

Mod

el

10 20 30 40 50 60 70 80

2040

6080

Quantile Plot

Model

Em

piric

al

1e-01 1e+00 1e+01 1e+02 1e+03

5010

015

0

Return Period

Ret

urn

Leve

l

Return Level Plot Density Plot

z

f(z)

0 20 40 60 80

0.00

0.01

0.02

0.03

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Probability Plot

Empirical

Mod

el

10 20 30 40 50 60 70

2040

6080

Quantile Plot

Model

Em

piric

al

1e-01 1e+00 1e+01 1e+02 1e+03

2040

6080

100

120

Return Period

Ret

urn

Leve

l

Return Level Plot Density Plot

z

f(z)

20 40 60 80

0.00

0.01

0.02

0.03

0.0 0.2 0.4 0.6 0.8 1.0

0.2

0.4

0.6

0.8

1.0

Probability Plot

Empirical

Mod

el

10 20 30 40 50 60 70

1020

3040

5060

Quantile Plot

Model

Em

piric

al

1e-01 1e+00 1e+01 1e+02 1e+03

2040

6080

120

Return Period

Ret

urn

Leve

l

Return Level Plot Density Plot

z

f(z)

10 20 30 40 50 60 70

0.00

0.01

0.02

0.03

0.04

0.05

Eastern

Mediterranean

Central

Mediterranean

Western

Mediterranean

Bayesian Method Bayesian Method

Eastern

Mediterranean

Central

Mediterranean

Western

Mediterranean

0 20 40 60 80 100

0.0

0.1

0.2

0.3

0.4

0.5

location parameter

De

nsi

ty

0 20 40 60 80 100

0.0

0.1

0.2

0.3

0.4

0.5

location parameter

De

nsi

ty

-20 0 20 40 60

0.0

0.1

0.2

0.3

0.4

0.5

scale parametre

De

nsi

ty

-20 0 20 40 60

0.0

0.1

0.2

0.3

0.4

0.5

scale parametre

De

nsi

ty

-4 -2 0 2 4

01

23

45

6

shape parameter

De

nsi

ty

-4 -2 0 2 4

01

23

45

6

shape parameter

De

nsi

ty

1 5 10 50 100 500 5000

05

01

00

15

02

00

return period

retu

rn le

vel

1 5 10 50 100 500 5000

05

01

00

15

02

00

return period

retu

rn le

vel

0 20 40 60 80 100

0.0

0.1

0.2

0.3

0.4

0.5

location parameter

De

nsi

ty

0 20 40 60 80 100

0.0

0.1

0.2

0.3

0.4

0.5

location parameter

De

nsi

ty

-20 0 20 40 60

0.0

0.1

0.2

0.3

0.4

0.5

scale parametre

De

nsi

ty

-20 0 20 40 60

0.0

0.1

0.2

0.3

0.4

0.5

scale parametre

De

nsi

ty

-4 -2 0 2 4

01

23

45

6

shape parameter

De

nsi

ty

-4 -2 0 2 4

01

23

45

6

shape parameter

De

nsi

ty

1 5 10 50 100 500 5000

05

01

00

15

02

00

return period

retu

rn le

vel

1 5 10 50 100 500 5000

05

01

00

15

02

00

return period

retu

rn le

vel

0 20 40 60 80 100

0.0

0.1

0.2

0.3

0.4

0.5

location parameter

De

nsi

ty

0 20 40 60 80 100

0.0

0.1

0.2

0.3

0.4

0.5

location parameter

De

nsi

ty

-20 0 20 40 60

0.0

0.1

0.2

0.3

0.4

0.5

scale parametre

De

nsi

ty

-20 0 20 40 60

0.0

0.1

0.2

0.3

0.4

0.5

scale parametre

De

nsi

ty

-4 -2 0 2 4

01

23

45

6

shape parameter

De

nsi

ty

-4 -2 0 2 4

01

23

45

6

shape parameter

De

nsi

ty

1 5 10 50 100 500 5000

05

01

00

15

02

00

return period

retu

rn le

vel

1 5 10 50 100 500 5000

05

01

00

15

02

00

return period

retu

rn le

vel

location scale shape Return level

Spatial distribution of maximum annual precipitation Spatial distribution of maximum annual precipitation

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

35

40

45

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

0 10 20 30 40 50 60 70 80 90 100

0 20 40 60 80 100 120 140 160 180 200

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

0 10 20 30 40 50 60 70 80 90 100

0 20 40 60 80 100 120 140 160 180 200

Max

Min

Mean

Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices

KNMI-MLEKNMI-MLE

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P m ( m m )

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

S c a l e ( σ )

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

l o c a t i o n ( μ )

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P 2 0 ( m m )

3 5

4 0

4 5

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 00 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices

KNMI - MLEKNMI - MLE

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P 1 0 0 ( m m )

3 5

4 0

4 5

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices

C4I - MLEC4I - MLE

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P m ( m m )

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

S c a l e ( σ )

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

l o c a t i o n ( μ )

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P 2 0 ( m m )

3 5

4 0

4 5

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices

C4I - MLEC4I - MLE

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P 1 0 0 ( m m )

3 5

4 0

4 5

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices

KNMI-BayesKNMI-Bayes

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P m

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

l o c a t i o n ( μ )

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

s c a l e ( σ )

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P 2 0

3 5

4 0

4 5

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices

KNMI - BayesKNMI - Bayes

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P 1 0 0

3 5

4 0

4 5

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices

C4I - BayesC4I - Bayes

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P m ( m m )

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

s c a l e ( σ )

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

l o c a t i o n ( μ )

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P 2 0

3 5

4 0

4 5

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 00 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices

C4I - BayesC4I - Bayes

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

P 1 0 0

3 5

4 0

4 5

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

Differences of the extreme precipitation indicesDifferences of the extreme precipitation indicesbetween the two time period (2031-2050 & 2081-2100) between the two time period (2031-2050 & 2081-2100)

and the reference period (1951-2000)and the reference period (1951-2000) KNMI-MLE KNMI-MLE

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2100-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2100-2000

35

40

45

-20 -15 -10 -5 0 5 10 15 20 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40

-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40-20 -15 -10 -5 0 5 10 15 20

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2100-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2100-2000

35

40

45

-20 -15 -10 -5 0 5 10 15 20 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40

-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40-20 -15 -10 -5 0 5 10 15 20

ΔPm

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP100

35

40

45

-200 -160 -120 -80 -40 0 40 80 120 160 200 -100 -80 -60 -40 -20 0 20 40 60 80 100

-200 -160 -120 -80 -40 0 40 80 120 160 200

ΔPm

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP100

35

40

45

-200 -160 -120 -80 -40 0 40 80 120 160 200 -100 -80 -60 -40 -20 0 20 40 60 80 100

-200 -160 -120 -80 -40 0 40 80 120 160 200

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2100-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2100-2000

35

40

45

-20 -15 -10 -5 0 5 10 15 20 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40

-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40-20 -15 -10 -5 0 5 10 15 20

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2100-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2100-2000

35

40

45

-20 -15 -10 -5 0 5 10 15 20 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40

-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40-20 -15 -10 -5 0 5 10 15 20

Differences of the extreme precipitation indicesDifferences of the extreme precipitation indicesbetween the time period (2031-2050) and the reference period between the time period (2031-2050) and the reference period

C4I-MLEC4I-MLEΔPm

-10 -5 0 5 10 15 20 25 30 35

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP100

35

40

45

-200 -160 -120 -80 -40 0 40 80 120 160 200 -100 -80 -60 -40 -20 0 20 40 60 80 100

-200 -160 -120 -80 -40 0 40 80 120 160 200

Differences of the extreme precipitation indicesDifferences of the extreme precipitation indicesbetween the two time period (2031-2050 & 2081-2100) between the two time period (2031-2050 & 2081-2100)

and the reference period (1951-2000)and the reference period (1951-2000)KNMI-BayesKNMI-Bayes

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2100-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2050-2000

35

40

45

-20 -15 -10 -5 0 5 10 15 20 -200 -160 -120 -80 -40 0 40 80 120 160 200

-20 -15 -10 -5 0 5 10 15 20 -200 -160 -120 -80 -40 0 40 80 120 160 200

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2100-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2100-2000

35

40

45

-20 -15 -10 -5 0 5 10 15 20 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40

-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40-20 -15 -10 -5 0 5 10 15 20

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2050-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔPm 2100-2000

35

40

45

-10 -5 0 5 10 15 20 25 30 35

ΔP20 2050-2000

35

40

45

-20 -15 -10 -5 0 5 10 15 20 -200 -160 -120 -80 -40 0 40 80 120 160 200

-20 -15 -10 -5 0 5 10 15 20 -200 -160 -120 -80 -40 0 40 80 120 160 200

Differences of the extreme precipitation indicesDifferences of the extreme precipitation indicesbetween the time period (2031-2050) and the reference period between the time period (2031-2050) and the reference period

C4I-BayesC4I-Bayes

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

Δ P m

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

Ä P 2 0

3 5

4 0

4 5

- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

Ä P 1 0 0

3 5

4 0

4 5

- 2 0 0 - 1 6 0 - 1 2 0 - 8 0 - 4 0 0 4 0 8 0 1 2 0 1 6 0 2 0 0 - 1 0 0 - 8 0 - 6 0 - 4 0 - 2 0 0 2 0 4 0 6 0 8 0 1 0 0

- 2 0 0 - 1 6 0 - 1 2 0 - 8 0 - 4 0 0 4 0 8 0 1 2 0 1 6 0 2 0 0

Concluding remarks

• The two RCMs datasets simulate reasonably well the extreme annual daily precipitation

• Pm index presents no change or a slight decrease for the future time period, in Mediterranean region

• P20, an index that locates in the tail of the GEV distribution, present increase especially in central Mediterranean

• The two estimators (MLE and Bayesian) present similar results for the reference period but different for the future time-period. The Bayesian method present a practical advantage.