1 Introduction

Compared to the observed changes in temperature, the changes in precipitation show more uncertainty (Hartmann et al. 2013). The IPCC AR5 indicated that anthropogenic forcing has contributed to a global-scale intensification of heavy precipitation since the second half of the twentieth century (IPCC 2013) and the intensity of daily precipitation increases more under the higher warming scenarios (Weber et al. 2018).

To achieve a comprehensive understanding of changes in precipitation in the future, this section initiatively assesses the change of precipitation characteristics, such as mean amount, variability, and extremes under three greenhouse gas emissions scenarios, including Representative Concentration Pathway (RCP) 2.6, RCP4.5, and RCP8.5.

2 Data

Same as the daily maximum and minimum temperature data, the global daily precipitation data were also retrieved from the NEX-GDDP dataset under RCP4.5 and RCP8.5 from 21 climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) (https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp). Furthermore, the precipitation data from 13 models in CMIP5 under the RCP2.6 scenario have also been downscaled by the Institute of Atmospheric Physics (IAP) Chinese Academy of Sciences (CAS) (Xu and Wang 2019). This dataset covers all grids between 60°S and 90°N global land area. The spatial resolution of the data for all maps is 0.25° (~25 km × 25 km).

3 Method

The precipitation extremes cover three time periods, including the historical period (1986–2005, denoted as the 2000s), and two future periods 2016–2035 (2030s) and 2046–2065 (2050s). Summer represents June–July–August (JJA), and winter is December and January–February (DJF) of the following year.

Total precipitation in wet days (Pr) is defined as the 20-year mean of summation of all daily precipitation amount ≥1 mm d−1 during the 2000s, 2030s, and 2050s. The Pr change (%) is defined as:

$$ Pr_{{{\text{change}}}} = {1}00\% \times \left( {Pr_{{{2}0{3}0{\text{s}}}} {-}Pr_{{{2}000{\text{s}}}} } \right)/Pr_{{{2}000{\text{s}}}} $$
(1)
$$ Pr_{{{\text{change}}}} = {1}00\% \times \left( {Pr_{{{2}0{5}0{\text{s}}}} {-}Pr_{{{2}000{\text{s}}}} } \right)/Pr_{{{2}000{\text{s}}}} $$
(2)

The inter-model uncertainty of Pr (ensemble spread) is defined as the standard deviation of the Prchange across all models.

Precipitation variability is defined as the standard deviation of Pr during three different periods. The change of precipitation variability during the 2030s and 2050s is calculated similarly as Eqs. (1) and (2), respectively. The inter-model uncertainty is the standard deviation of the precipitation variability across all models.

Precipitation extreme indices, including RX1day, RX5day, and R10mm, are adopted from the Expert Team on Climate Change Detection and Indices (ETCCDI, see Klein Tank et al. (2009) and Zhang et al. (2011)).

RX1day is the maximum 1-day precipitation amount (mm/day). R10mm is the number of days when daily precipitation amount ≥10 mm. RX5day is the maximum consecutive 5-day precipitation. The definition of RX1day change, RX5day change, and R10mm change and their inter-model uncertainty are similarly defined as those in Eqs. (1) and (2).

4 Major Findings

Nine regions were selected following Giorgi and Bi (2005) to quantitatively compare the changes of precipitation under the three greenhouse gas emissions scenarios. These regions are sensitive to global warming (Xu et al. 2019). Figures 1, 2, 3, 4 and 5 show the area-weighted average annual total precipitation in wet days, precipitation variability, annual maximum 1-day precipitation (RX1D), annual days of daily precipitation equal to or greater than 10 mm (R10mm), and annual maximum consecutive 5-day precipitation (RX5D), respectively. Generally, the changes in precipitation depend on the greenhouse gas emissions scenario and the region.

Fig. 1
figure 1

Annual total precipitation in wet days (unit: mm) in nine regions under different Representative Concentration Pathway (RCP) scenarios. The error bar represents the one standard deviation across all selected models—13 general circulation models (GCMs) (RCP2.6) and 21 GCMs (RCP4.5 and RCP8.5). NAS, MED, NAU, SQF, AMZ, TIB, EAS, SEA, and ENA represent North Asia (47–70°N, 60.5–180.5°E), Mediterranean Basin (30–47°N, 10.5°W–37.5°E), Northern Australia (28–10°S, 109.5–155.5°E), South Equatorial Africa (26–0°S, 0.5–55.5°E), Amazon Basin (20°S–10°N, 78.5–34.5°W), Tibet (30–47°N, 80.5–104.5°E), East Asia (20–47°N, 104.5–140.5°E), Southeast Asia (10°S–20°N, 100.5–150.5°E), and Eastern North America (25–50°N, 85.5–60.5°W), respectively. The regional division follows Giorgi and Bi (2005)

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Fig. 2
figure 2

Variability of annual total precipitation in wet days (unit: mm) in nine regions under different Representative Concentration Pathway (RCP) scenarios. The error bar represents the one standard deviation across all selected models. Region abbreviations are the same as in Fig. 1

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Fig. 3
figure 3

Annual maximum 1-day precipitation (unit: mm) in nine regions under different Representative Concentration Pathway (RCP) scenarios. The error bar represents the one standard deviation across all selected models. Region abbreviations are the same as in Fig. 1

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Fig. 4
figure 4

Annual days of daily precipitation equal to or greater than 10 mm (unit: d) in nine regions under different Representative Concentration Pathway (RCP) scenarios. The error bar represents the one standard deviation across all selected models. Region abbreviations are the same as in Fig. 1

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Fig. 5
figure 5

Annual maximum consecutive 5-day precipitation (unit: mm) in nine regions under different Representative Concentration Pathway (RCP) scenarios. The error bar represents the one standard deviation across all selected models. Region abbreviations are the same as in Fig. 1

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5 Maps

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