Strong increase in convective precipitation in response to higher temperatures

Journal name:
Nature Geoscience
Year published:
Published online

Precipitation changes can affect society more directly than variations in most other meteorological observables1, 2, 3, but precipitation is difficult to characterize because of fluctuations on nearly all temporal and spatial scales. In addition, the intensity of extreme precipitation rises markedly at higher temperature4, 5, 6, 7, 8, 9, faster than the rate of increase in the atmosphere’s water-holding capacity1, 4, termed the Clausius–Clapeyron rate. Invigoration of convective precipitation (such as thunderstorms) has been favoured over a rise in stratiform precipitation (such as large-scale frontal precipitation) as a cause for this increase4, 10, but the relative contributions of these two types of precipitation have been difficult to disentangle. Here we combine large data sets from radar measurements and rain gauges over Germany with corresponding synoptic observations and temperature records, and separate convective and stratiform precipitation events by cloud observations. We find that for stratiform precipitation, extremes increase with temperature at approximately the Clausius–Clapeyron rate, without characteristic scales. In contrast, convective precipitation exhibits characteristic spatial and temporal scales, and its intensity in response to warming exceeds the Clausius–Clapeyron rate. We conclude that convective precipitation responds much more sensitively to temperature increases than stratiform precipitation, and increasingly dominates events of extreme precipitation.

At a glance


  1. Separation of precipitation types.
    Figure 1: Separation of precipitation types.

    a, Map of the investigation area with radar (grey circles), synoptic (red crosses) and precipitation gauge stations (blue dots). The quadrants used for the radar processing are shown as dashed lines. The Lindenberg station for comparison with the bright-band method (see Methods) is marked with a bold green cross. b, Example of a dominant stratiform synoptic weather condition. The symbols S and M mark observations of stratiform and mixed synoptic cloud conditions, respectively radar-observed rain intensity is marked as grey shades. c, The same as for b but for dominant convective (C) conditions.

  2. Probability distribution of precipitation intensity.
    Figure 2: Probability distribution of precipitation intensity.

    a, Five-minute precipitation intensity distribution for convective (blue), stratiform (red) and total precipitation (black) from gauges. Dashed line: power-law fit (log–log axes). b, The relative contribution of convective precipitation to the sum of the two types of precipitation as a function of intensity (logarithmic horizontal scale). c, Intensity percentiles of convective (blue), stratiform (red) and total precipitation (black) for the 75th (solid) and 99th (long-dashed) percentiles. Solid and dashed purple lines mark 7% °C−1 and 14% °C−1 increases, respectively (logarithmic vertical axis). d, The same as for b but as a function of temperature. Shaded areas denote the 90% confidence intervals computed by bootstrapping.

  3. Event intensity profile and correlations.
    Figure 3: Event intensity profile and correlations.

    a, Intensity distributions of stratiform events with areas 100–400km2 for temperatures 2.5–22.5°C from radar data. Increasing temperatures (5°C steps) are shown in colours from blue to red. b, The same as for a but for convection. Dashed lines are power-law fits to curves in a. Vertical dashed lines indicate distribution means. c, Distribution of event-mean intensity conditional on area. Blue and red shades denote convective and stratiform precipitation, respectively. Areas range from 10–400km2 (thin to thick lines). Arrows indicate increasing area. Note log–log axes.

  4. Event scaling in time and space.
    Figure 4: Event scaling in time and space.

    Characteristics of convective (blue) and stratiform (red) events for gauges (left and central column) and radar (right). a, Distributions of event duration. Dashed line indicates power-law fit. b, Temperature dependence of event occurrence conditional on duration. c, The same as a for event area. d, Duration dependence for average (circles) and extreme events (95th percentile, triangles). e, Temperature dependence of average (lines) and extreme events (triangles) conditional on duration. Solid and dashed lines indicate 7% °C−1 and 14% °C−1 increases, respectively. f, The same as for d for event area. Note the vertical (horizontal) log-scales in all panels (left and right columns).


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  1. Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology, Wolfgang-Gaede-Weg 1, 76131 Karlsruhe, Germany

    • Peter Berg
  2. Rossby Centre, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 6017631 Norrköping, Sweden

    • Peter Berg
  3. Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany

    • Christopher Moseley
  4. Helmholtz Zentrum Geesthacht, Climate Service Center, Fischertwiete 1, 200956 Hamburg, Germany

    • Christopher Moseley
  5. Center for Models of Life, Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark

    • Jan O. Haerter


P.B. processed the synoptic and station data, performed data analysis and contributed to the manuscript. C.M. performed the preparation of the radar data and contributed to the manuscript. J.O.H. performed the radar data analysis and contributed to the manuscript. The initial idea was equally conceived by P.B. and J.O.H.

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