The precipitation risk estimates the intensification of heavy rain events, which can damage the asset. It is assessed with three intensity indices (SDII, R95pTOT, R99pTOT) by measuring how rare future events become relative to the past: the more frequent intense events become, the higher the risk. Of the three indices the worst is taken.
Indicator. Simple Daily Intensity Index (SDII), in mm/day; Very Wet Day precipitation (R95pTOT), in mm; Extremely Wet Day precipitation (R99pTOT), in mm.
Data. Variables SDII, R95pTOT and R99pTOT from the Copernicus Climate Data Store (CDS); for each one a GEV distribution is pre-fitted on the column of maxima among the climate model members. Historical period: 1850–1970. Scenarios: SSP1-2.6 / SSP2-4.5 / SSP3-7.0 / SSP5-8.5.
Calculation. For each (scenario, year) pair the return period is computed, for each index, via the GEV survival function, capped at 250 years. For the comparison with the GEV the multi-model maximum (x.max) is used, not the mean, because the GEV is fitted on the historical annual maxima across the models. Each return period is mapped to a level 0–5; the final level is the maximum of the three.
Thresholds → level A–F.
• For each of the three indices, based on the return period (years), thresholds [5, 20, 50, 100, 250]: A: RP < 5
• B: 5–20
• C: 20–50
• D: 50–100
• E: 100–250
• F: ≥ 250
• the final level is the maximum of the three sub-levels.
The 1850–1970 historical period and the 5 / 20 / 50 / 100 / 250-year thresholds are xNatura choices without a published source. The values shown are the mean of the climate models, while the class uses the highest value among the models.
From physical to impact. The physical level (A–F) is combined with the production process's sensitivity through the risk matrix (6 physical levels by 5 sensitivity bands) → impact level. The euro amount of this risk is the one described in the "Production damage" paragraph (Version 2026): the damage factor per class of Version 2025 is no longer used for this risk.
Production damage. Rain is counted for what it does to the work of the site rather than to the building: the value added lost is estimated, in euro per employee per year (multiplied by the site's employees). Basis: "xNatura estimate", because a regional estimate is applied to one site.
• Effect: Kotz, Levermann and Wenz 2022 measure, on 1,554 regions from 1979 to 2019, how the output of manufacturing and of services changes with the yearly rain of the days above the local 99.9th percentile of daily rain; the effect per mm is about -0.02% of output and smaller in warmer regions. Manufacturing coefficients for industry, mining, energy, water and construction, services coefficients for the other activities (an xNatura mapping); agriculture is not significant in the study and gets no loss.
• Climate: for each of ten climate models (the ISIMIP3b set), NASA NEX-GDDP-CMIP6 daily rain, cells of 0.25° (about 25 km), corrected on observations (Thrasher et al. 2022); change between the 20 years around the year and 1995-2014, then the mean of the models.
• The change is applied as a lasting change of the level of output, as Waidelich et al. 2024 do with the same functions. Where it rains less than in 1995-2014 the study implies a small gain, shown as 0.
• Euro: share lost × yearly value added per employed person of the country and sector of the site's production processes, as for heat stress and air quality (Eurostat in Europe, World Bank and ILO outside).
Sources.
• [Copernicus Climate Data Store (CDS)](Copernicus Climate Data Store (CDS))
• [Indices for monitoring changes in extremes based on daily temperature and precipitation data](Indices for monitoring changes in extremes based on daily temperature and precipitation data)
• [ETCCDI Climate Change Indices](ETCCDI Climate Change Indices)
• [Climate Change 2021: The Physical Science Basis](Climate Change 2021: The Physical Science Basis)
• [Kotz et al. 2022, The effect of rainfall changes on economic production](Kotz et al. 2022, The effect of rainfall changes on economic production)
• [Waidelich et al. 2024, Climate damage projections beyond annual temperature](Waidelich et al. 2024, Climate damage projections beyond annual temperature)
• [Thrasher et al. 2022, NASA Global Daily Downscaled Projections, CMIP6](Thrasher et al. 2022, NASA Global Daily Downscaled Projections, CMIP6)
• [Eurostat, national accounts by industry (nama_10_a64)](Eurostat, national accounts by industry (nama_10_a64))
• [World Bank, gross value added at basic prices](World Bank, gross value added at basic prices)
• [ILOSTAT, employment (ILO modelled estimates)](ILOSTAT, employment (ILO modelled estimates)).
Indicator. Simple Daily Intensity Index (SDII), in mm/day; Very Wet Day precipitation (R95pTOT), in mm; Extremely Wet Day precipitation (R99pTOT), in mm.
Data. Variables SDII, R95pTOT and R99pTOT from the Copernicus Climate Data Store (CDS); for each one a GEV distribution is pre-fitted on the column of maxima among the climate model members. Historical period: 1850–1970. Scenarios: SSP1-2.6 / SSP2-4.5 / SSP3-7.0 / SSP5-8.5.
Calculation. For each (scenario, year) pair the return period is computed, for each index, via the GEV survival function, capped at 250 years. For the comparison with the GEV the multi-model maximum (x.max) is used, not the mean, because the GEV is fitted on the historical annual maxima across the models. Each return period is mapped to a level 0–5; the final level is the maximum of the three.
Thresholds → level A–F.
• For each of the three indices, based on the return period (years), thresholds [5, 20, 50, 100, 250]: A: RP < 5
• B: 5–20
• C: 20–50
• D: 50–100
• E: 100–250
• F: ≥ 250
• the final level is the maximum of the three sub-levels.
The 1850–1970 historical period and the 5 / 20 / 50 / 100 / 250-year thresholds are xNatura choices without a published source. The values shown are the mean of the climate models, while the class uses the highest value among the models.
From physical to impact. The physical level (A–F) is combined with the production process's sensitivity through the risk matrix (6 physical levels by 5 sensitivity bands) → impact level. The euro amount of this risk is the one described in the "Production damage" paragraph (Version 2026): the damage factor per class of Version 2025 is no longer used for this risk.
Production damage. Rain is counted for what it does to the work of the site rather than to the building: the value added lost is estimated, in euro per employee per year (multiplied by the site's employees). Basis: "xNatura estimate", because a regional estimate is applied to one site.
• Effect: Kotz, Levermann and Wenz 2022 measure, on 1,554 regions from 1979 to 2019, how the output of manufacturing and of services changes with the yearly rain of the days above the local 99.9th percentile of daily rain; the effect per mm is about -0.02% of output and smaller in warmer regions. Manufacturing coefficients for industry, mining, energy, water and construction, services coefficients for the other activities (an xNatura mapping); agriculture is not significant in the study and gets no loss.
• Climate: for each of ten climate models (the ISIMIP3b set), NASA NEX-GDDP-CMIP6 daily rain, cells of 0.25° (about 25 km), corrected on observations (Thrasher et al. 2022); change between the 20 years around the year and 1995-2014, then the mean of the models.
• The change is applied as a lasting change of the level of output, as Waidelich et al. 2024 do with the same functions. Where it rains less than in 1995-2014 the study implies a small gain, shown as 0.
• Euro: share lost × yearly value added per employed person of the country and sector of the site's production processes, as for heat stress and air quality (Eurostat in Europe, World Bank and ILO outside).
Sources.
• [Copernicus Climate Data Store (CDS)](Copernicus Climate Data Store (CDS))
• [Indices for monitoring changes in extremes based on daily temperature and precipitation data](Indices for monitoring changes in extremes based on daily temperature and precipitation data)
• [ETCCDI Climate Change Indices](ETCCDI Climate Change Indices)
• [Climate Change 2021: The Physical Science Basis](Climate Change 2021: The Physical Science Basis)
• [Kotz et al. 2022, The effect of rainfall changes on economic production](Kotz et al. 2022, The effect of rainfall changes on economic production)
• [Waidelich et al. 2024, Climate damage projections beyond annual temperature](Waidelich et al. 2024, Climate damage projections beyond annual temperature)
• [Thrasher et al. 2022, NASA Global Daily Downscaled Projections, CMIP6](Thrasher et al. 2022, NASA Global Daily Downscaled Projections, CMIP6)
• [Eurostat, national accounts by industry (nama_10_a64)](Eurostat, national accounts by industry (nama_10_a64))
• [World Bank, gross value added at basic prices](World Bank, gross value added at basic prices)
• [ILOSTAT, employment (ILO modelled estimates)](ILOSTAT, employment (ILO modelled estimates)).