Downscaling of Future Precipitation in China’s Beijing-Tianjin-Hebei Region Using a Weather Generator

: To project local precipitation at the existing meteorological stations in China’s Beijing-Tianjin-Hebei region in the future, local daily precipitation was simulated for three periods (2006–2030, 2031–2050, and 2051–2070) under RCP 4.5 and RCP 8.5 emission scenarios. These projections were statistically downscaled using a weather generator (BCC/RCG-WG) and the output of ﬁve global climate models. Based on the downscaled daily precipitation at 174 stations, eight indices describing mean and extreme precipitation climates were calculated. Overall increasing trends in the frequency and intensity of the mean and extreme precipitation were identiﬁed for the majority of the stations studied, which is in line with the GCMs’ output. However, the downscaling approach enables more local features to be reﬂected, adding value to applications at the local scale. Compared with the baseline during 1961–2005, the regional average annual precipitation and its intensity are projected to increase in all three future periods under both RCP 4.5 and RCP 8.5. The projected changes in the number of days with precipitation are relatively small across the Beijing-Tianjin-Hebei region. The regional average annual number of days with precipitation would increase by 0.2~1.0% under both RCP 4.5 and RCP 8.5, except during 2031–2050 under RCP 8.5 when it would decrease by 0.7%. The regional averages of annual days with precipitation ≥ 25 mm and ≥ 40 mm, the greatest one-day and ﬁve-day precipitation in the Beijing-Tianjin-Hebei region, are projected to increase by 8~30% during all the three periods. The number of days with daily precipitation ≥ 40 mm was projected to increase most signiﬁcantly out of the eight indices, indicating the need to consider increased ﬂooding risk in the future. The average annual maximum number of consecutive days without precipitation in the Beijing-Tianjin-Hebei region is projected to decrease, and the drought risk in this area is expected to decrease.


Introduction
China's Beijing-Tianjin-Hebei region, located at 113.06 to 119.88 • E, 36.02 to 42.62 • N, includes the Beijing and Tianjin municipalities, and 11 other cities in Hebei Province. The region is one of the major grain production areas as well as the largest urban agglomeration in China [1]. The region covers 216,000 km 2 , has a population of 130 million, and accounted for nearly 10% of China's gross domestic product (GDP) in 2017 [2]. The Xiongan New Area in Hebei Province, which has been under construction since 2017 to ease Beijing's non-capital function, is known as a major national element of China's millennium plan [3].
The terrain of the Beijing-Tianjin-Hebei region is high in the northwest and low in the southeast. From northwest to southeast, the region includes the Bashang Plateau, Yanshan Mountains, Taihang Mountains, and Hebei Plain. The annual average precipitation decreases gradually from southeast to northwest. It is more than 600 mm in parts of the Yanshan area and Hebei Plain, and less than 400 mm on the Bashang Plateau. With a warm

Precipitation Data from Observations and GCMs
Daily precipitation observations at 174 meteorological stations in the Beijing-Tianjin-Hebei region from 1961 to 2005 were used. These data were provided by the National Meteorological Information Center of the China Meteorological Administration. Daily precipitation data at each grid box in the past and future under the RCP 4.5 and RCP 8.5 emission scenarios were obtained from five CMIP5 GCMs [41]: CNRM-CM5, CSIRO-Mk3-6-0, EC-EARTH, MIROC-ESM-CHEM, and NorESM1-M, which have been evaluated and selected as the best climate models for northern China [42]. Because the five GCMs use different grid systems, all GCMs data were bi-linearly interpolated to the grid of 1.0 • lon × 1.0 • lat to facilitate the calculation. The control run from 1961 to 2005 represents past climate and scenarios, while the run from 2006 to 2070 represents future climate conditions. The locations of the meteorological stations and the grid boxes' layout of the GCMs used in the study are shown in Figure 1. The density of stations varies across the region, and is lower in the northern and western mountain areas.

Precipitation Indices
Eight precipitation indices (seen in Table 1) were used in this study to explore various properties of local precipitation in the historical and future periods across the Beijing-Tianjin-Hebei region, focusing on the occurrence and severity of extreme events [43,44]. Generally, the daily precipitation intensity, days of precipitation ≥0.1 mm, and the amount of precipitation are used to represent average precipitation conditions. The numbers of days with precipitation ≥25 mm and ≥40 mm are indices representing rare extreme and very rare extreme precipitation events. The greatest total one-day and five-day precipitation were chosen as short-term and longer-term extremes measures, respectively. Drought risk can be expressed by the maximum number of consecutive days without precipitation. Table 1. Description of the eight precipitation indices used in the study.

Index
Description (Unit) Arain Amount of annual precipitation (mm) Nrain Number of days with precipitation ≥0.1 mm (d) Pint Daily precipitation intensity (precipitation total per rainy day, mm/d) Exc25 Number of days with precipitation ≥25 mm (d) Exc40 Number of days with precipitation ≥40 mm (d) Px1d Greatest one-day precipitation (mm) Px5d Greatest five-day total precipitation (mm) Pxcdd Maximum number of consecutive days without precipitation (d)

Calculation of WG Parameters
A key component of the downscaling approach in this study is the WG that was used. BCC/RCG-WG was chosen because it has been proven to be skillful in simulating daily variables in China including precipitation, air temperature, sunshine, wind, and relative humidity [45][46][47][48][49]. The BCC/RCG-WG simulates the occurrence and intensity of precipitation at a given station in two separate steps. The first step determines whether a certain day is wet or dry using a two-state, first order Markov Chain. This step involves two transition probabilities: dry day to wet day (Pwd), and wet day to wet day (Pww). The precipitation for a wet day was determined in the second step with a two-parameter

Precipitation Indices
Eight precipitation indices (seen in Table 1) were used in this study to explore various properties of local precipitation in the historical and future periods across the Beijing-Tianjin-Hebei region, focusing on the occurrence and severity of extreme events [43,44]. Generally, the daily precipitation intensity, days of precipitation ≥0.1 mm, and the amount of precipitation are used to represent average precipitation conditions. The numbers of days with precipitation ≥25 mm and ≥40 mm are indices representing rare extreme and very rare extreme precipitation events. The greatest total one-day and five-day precipitation were chosen as short-term and longer-term extremes measures, respectively. Drought risk can be expressed by the maximum number of consecutive days without precipitation. Table 1. Description of the eight precipitation indices used in the study.

Index Description (Unit)
Arain Amount of annual precipitation (mm) Nrain Number of days with precipitation ≥0.1 mm (d) Pint Daily precipitation intensity (precipitation total per rainy day, mm/d) Exc25 Number of days with precipitation ≥25 mm (d) Exc40 Number of days with precipitation ≥40 mm (d) Px1d Greatest one-day precipitation (mm) Px5d Greatest five-day total precipitation (mm) Pxcdd Maximum number of consecutive days without precipitation (d)

Calculation of WG Parameters
A key component of the downscaling approach in this study is the WG that was used. BCC/RCG-WG was chosen because it has been proven to be skillful in simulating daily variables in China including precipitation, air temperature, sunshine, wind, and relative humidity [45][46][47][48][49]. The BCC/RCG-WG simulates the occurrence and intensity of precipitation at a given station in two separate steps. The first step determines whether a certain day is wet or dry using a two-state, first order Markov Chain. This step involves two transition probabilities: dry day to wet day (Pwd), and wet day to wet day (Pww). The precipitation for a wet day was determined in the second step with a two-parameter (shape parameter alpha and scale parameter beta) gamma distribution function. When simulating daily precipitation, monthly transition probabilities were used in the first step to determine whether a certain day was wet or dry. The precipitation for each wet day was simulated using the monthly parameters of the gamma distribution. For stochastic generation of precipitation amounts, the parameters for the distribution function should be computed from no fewer than 20 years of historical records [50]. The historical transition probabilities and alpha and beta parameters of 174 stations across the Beijing-Tianjin-Hebei region were calculated for each month using the observed daily precipitation records from 1961 to 2005. The monthly precipitation simulation parameters for each grid box containing at least one precipitation station in the Beijing-Tianjin-Hebei region were computed using daily precipitation data from the control (1961 to 2005) and scenario run (2006 to 2030, 2031 to 2050 and 2051 to 2070). Seven sets of WG parameters for each grid box were produced for each GCM: one set for the control run, and the other six sets for the scenario runs (the two climate scenarios RCP 4.5 and RCP 8. To project daily precipitations at local stations in the future, it is necessary to determine the parameters that represent the future climate conditions. In this study, local parameters were obtained by assuming uniform changes in parameters for all the stations within a given GCM grid. This assumption was considered reasonable [51][52][53][54], and a new set of future WG parameters was generated in each period for each meteorological station in the region. Overall, a set of 48 parameters was obtained for all stations (four WG parameters for 12 months) for each of the three periods 2006 to 2030, 2031 to 2050, and 2051 to 2070 respectively. These parameters were then used to simulate daily precipitation in the future for those three periods.

Precipitation Simulation and Future Changes in Indices
Based on the WG parameters for the observed and projected climate, the precipitation at each meteorological station in the past and under the emission scenarios RCP 4.5 and RCP 8.5 for the five GCMs during 2006-2030, 2031-2050, and 2051-2070 were simulated. In order to reasonably represent the climate conditions, 100 years of daily precipitation were simulated. Although historical records cover 45 years and each GCM time span in the future only covers 20 or 25 years, the WG simulated time series for 100 years was used to obtain higher statistical confidence in the simulated precipitation. It is very important when using such simulations to derive statistics for extreme climate events.
All eight precipitation indices in Table 1 were calculated for each meteorological station from the simulation series in the past and under emission scenarios of RCP 4.5 and RCP 8.5 for the five GCMs during the three periods. Then, the relative changes between the observation-based and scenario-based indices under the emission scenarios of RCP 4.5 and RCP 8.5 for five GCMs during the three periods were calculated to quantify the change in the local climate of precipitation.

Performance of the Precipitation Generator
The simulated precipitation results were compared against the observations to assess the quality of the simulations. Eight indices were derived from the simulated 100 years and compared with the corresponding observed statistics for 1961-2005. The scatter plots in Figure 2 compare the annual indices of precipitation from the observations and simulations. Each dot corresponds to one station. For Arain and Pint, the simulations agreed well with the observed records. The best agreement was achieved for the annual amount of precipitation. For Nrain, the simulations slightly, but systematically, overestimated the number days with precipitation. On the other hand, the simulated extreme indices such as Exc25, Exc40, Px1d, Px5d, and Pxcdd were underestimated at almost all stations. Because the occurrence of such extreme values is very rare and the historical records are quite short, it is difficult to fit the tail of the gamma distribution function to the observed records [55]. Increasing the length of the historical records would improve the accuracy of parameters calculated from those records, and may improve the simulations.
agreed well with the observed records. The best agreement was achieved for the annual amount of precipitation. For Nrain, the simulations slightly, but systematically, overestimated the number days with precipitation. On the other hand, the simulated extreme indices such as Exc25, Exc40, Px1d, Px5d, and Pxcdd were underestimated at almost all stations. Because the occurrence of such extreme values is very rare and the historical records are quite short, it is difficult to fit the tail of the gamma distribution function to the observed records [55]. Increasing the length of the historical records would improve the accuracy of parameters calculated from those records, and may improve the simulations.  Table 2 shows the regional average percentage changes in the eight precipitation indices in the Beijing-Tianjin-Hebei region during the future three periods calculated from the raw GCM outputs and downscaling data by WG. It can be seen that the changes in the trends of most indices are, in principle, consistent before and after downscaling. Table 2. Comparison of regional average percentage changes in the eight precipitation indices in the Beijing-Tianjin-Hebei region during the future three periods calculated from the raw GCM outputs and downscaling data by WG (unit: %).  Table 2 shows the regional average percentage changes in the eight precipitation indices in the Beijing-Tianjin-Hebei region during the future three periods calculated from the raw GCM outputs and downscaling data by WG. It can be seen that the changes in the trends of most indices are, in principle, consistent before and after downscaling. Table 2. Comparison of regional average percentage changes in the eight precipitation indices in the Beijing-Tianjin-Hebei region during the future three periods calculated from the raw GCM outputs and downscaling data by WG (unit: %).

Changes in Precipitation
The percentage changes between the observation-based and scenario-based indices derived from the five GCMs were ensemble averaged to quantify the variation of the precipitation climate in the future. Arain, Nrain, and Pint are the basic indices representing the precipitation conditions. The regional averages of the percentage changes in the three indices simulated by the five GCMs are shown in Table 3, along with their ensemble mean under the RCP 4.5 and RCP 8.5 emission scenarios. In general, the simulations suggest a change toward a wetter climate at the majority of stations in the Beijing-Tianjin-Hebei region in the future, which is the opposite to the trend in the past [8,[56][57][58], but consistent with most other studies [32,59,60]. Under the influence of global change, with the increasing concentration of greenhouse gases and aerosols, the East Asian summer monsoon is expected to increase, the rain belt in East China may move northward, and the precipitation in North China would increase in summer [61][62][63]. However, the magnitude and spatial distribution of the changes vary between each of the three indices.   According to the raw data statistics, these two GCMs also showed a drying trend in these two periods. Under the RCP 8.5 emission scenario, annual precipitation in the future also showed an increasing trend. However, the magnitudes of these changes during 2006-2030 and 2031-2050 were 5.6% and 5.1%, respectively, both less than the changes projected under RCP 4.5. The expected change for 2051-2070 was 10.3%, which was more than that for RCP 4.5. The regional average increases over Beijing, Tianjin, and the Xiongan New Area were 12.3%, 11.7%, and 9.3%, respectively (Table 4) For most GCMs, the number of future precipitation day changed much less than the precipitation amount under both the RCP 4.5 and RCP 8.5 emission scenarios. Some GCMs showed increasing precipitation days, while others showed decreases. Averaged over the five GCMs used, the regional average annual Nrain across the Beijing-Tianjin-Hebei region would increase by 0.2%, 0.5%, and 0.4% during 2006-2030, 2031-2050, and 2051-2070, respectively, under RCP 4.5 when compared to the control period. Under RCP 8.5, the average percentage changes were 0.6%, −0.7%, and 1.0% in the future three periods.
Averaged across the five GCMs, the annual precipitation and rainy days would have an increasing trend in the future at most stations, but the former will increase more than the latter, so annual Pint tends to increase at most stations. The regional average changes were projected to be 7.4%, 7.9%, and 8.3% during 2006-2030, 2031-2050, and 2051-2070, respectively, under RCP 4.5, and 4.9%, 5.7%, and 9.3%, respectively, under RCP 8.5. Therefore, the intensity of precipitation would continue to increase under both emission scenarios. In this period, the increase is projected to exceed 10% at most northern stations, and the regional average increases over Beijing and Tianjin would reach 11.6% and 11.3%, respectively ( Table 4). The increase in precipitation is very beneficial in alleviating the drought and water shortage in the Beijing-Tianjin-Hebei region. However, we need to pay more attention to possible rainstorms, floods, and urban waterlogging disasters in the future [64].
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Changes of Extreme Precipitation
Extreme precipitation events can be characterized by indices Exc25, Exc40, Px1d, and Px5d. The regional average percentage changes in the four extreme precipitation indices simulated by the five GCMs and their average under RCP 4.5 and RCP 8.5 are shown in Table 3. Generally, the simulations suggest a change toward more extreme precipitation events across the Beijing-Tianjin-Hebei region in the future. This is consistent with the previous research results [21,32,65]. However, the range and spatial distribution of the changes in the four indices depend on models used and the period analyzed. Firstly

Changes of Extreme Precipitation
Extreme precipitation events can be characterized by indices Exc25, Exc40, Px1d, and Px5d. The regional average percentage changes in the four extreme precipitation indices simulated by the five GCMs and their average under RCP 4.5 and RCP 8.5 are shown in Table 3. Generally, the simulations suggest a change toward more extreme precipitation events across the Beijing-Tianjin-Hebei region in the future. This is consistent with the previous research results [21,32,65]. However, the range and spatial distribution of the changes in the four indices depend on models used and the period analyzed.  Figure 4 shows the spatial distribution of changes of Exc25 in the Beijing-Tianjin-Hebei region during the three time periods compared with 1961-2005. Annual Exc25 tended to increase at most stations under RCP 4.5 and RCP 8.5 emission scenarios. In 2051-2070 under RCP 8.5, this index would increase more than 20% in most northern stations, even increasing by more than 50% in some northwest stations. The regional average percentage increase over Beijing reaches 22.5% (Table 4). However, annual Exc25 does tend to decrease at a few central stations under RCP 8.5. , x FOR PEER REVIEW 10 of 18 tions. The regional average percentage increase over Beijing reaches 22.5% (Table 4). However, annual Exc25 does tend to decrease at a few central stations under RCP 8.5. For most GCMs, the increase in days with precipitation ≥40 mm was obviously greater than the increase in days with precipitation ≥25 mm under both RCP 4.5 and RCP 8.5. Averaged over five GCMs, this increase was the largest among the eight indices. This indicates that rare precipitation events with daily precipitation above 40 mm are expected to occur more frequently in the future. The regional average of annual Exc40 across the Beijing-Tianjin-Hebei region would increase by 25 Figure 5 shows the spatial distribution of percentage changes of Exc40 in the Beijing-Tianjin-Hebei region during the three time periods compared with 1961-2005. Annual Exc40 is projected to increase at most stations in the future, especially at some northwest stations, where the magnitude of the increase is greater than 50%. The northwest Beijing-Tianjin-Hebei region is mainly mountainous, and thus more attention should be paid to the prevention of mountain torrents caused by heavy rainfall in the future [32]. There are only a few stations where the annual Exc40 is projected to decrease in the future. For most GCMs, the increase in days with precipitation ≥40 mm was obviously greater than the increase in days with precipitation ≥25 mm under both RCP 4.5 and RCP 8.5. Averaged over five GCMs, this increase was the largest among the eight indices. This indicates that rare precipitation events with daily precipitation above 40 mm are expected to occur more frequently in the future. The regional average of annual Exc40 across the Beijing-Tianjin-Hebei region would increase by 25 Figure 5 shows the spatial distribution of percentage changes of Exc40 in the Beijing-Tianjin-Hebei region during the three time periods compared with 1961-2005. Annual Exc40 is projected to increase at most stations in the future, especially at some northwest stations, where the magnitude of the increase is greater than 50%. The northwest Beijing-Tianjin-Hebei region is mainly mountainous, and thus more attention should be paid to the prevention of mountain torrents caused by heavy rainfall in the future [32]. There are only a few stations where the annual Exc40 is projected to decrease in the future.  11.4%, which is more than that under RCP 4.5. Figure 6 shows the spatial distribution of percentage changes of Px1d across the Beijing-Tianjin-Hebei region during the three time periods compared with 1961-2005. The variation in Px1d between stations was larger than that of the former indices, which demonstrates the need for downscaling. The increase at some stations would be more than 40%, while at other stations in the southern and central Beijing-Tianjin-Hebei region, this index is expected to decrease by more than 20%. Specifically, 25  The change for 2031-2050 was 11.4%, which is more than that under RCP 4.5. Figure 6 shows the spatial distribution of percentage changes of Px1d across the Beijing-Tianjin-Hebei region during the three time periods compared with 1961-2005. The variation in Px1d between stations was larger than that of the former indices, which demonstrates the need for downscaling. The increase at some stations would be more than 40%, while at other stations in the southern and central Beijing-Tianjin-Hebei region, this index is expected to decrease by more than 20%. Specifically, 25    showed a decreasing trend in Px5d, which is contrary to most other GCMs. Under RCP 8.5, the averaged greatest five-day precipitation in the Beijing-Tianjin-Hebei region also showed an increasing trend. However, the projected changes in Px5d were 11.9% and 9.1% during 2006-2030 and 2051-2070, respectively, which were less than those of RCP 4.5. The change for 2031-2050 was 12.3%, greater than that of RCP 4.5. Figure 7 shows the spatial distribution of the percentage changes of Px5d across the Beijing-Tianjin-Hebei region during the three time periods compared with 1961-2005. Similar to Px1d, Px5d showed a large spatial variance. The increase in some stations would be more than 40%, while in some other stations in the southern and central Beijing-Tianjin-Hebei region, the decrease was larger than 20%.

Changes to Drought Risk
The maximum number of consecutive days without precipitation is a useful indication of drought risk [66,67]. Under RCP 4.5, the five-GCM average changes of Pxcdd across the Beijing-Tianjin-Hebei region during 2006-2030, 2031-2050, and 2051-2070 were −0.3%, −0.5%, and −0.2%, respectively. Under RCP 8.5, the regional average Pxcdd in the Beijing-Tianjin-Hebei region also showed a decreasing trend, but the range of this

Changes to Drought Risk
The maximum number of consecutive days without precipitation is a useful indication of drought risk [66,67]. Under −0.5%, and −0.2%, respectively. Under RCP 8.5, the regional average Pxcdd in the Beijing-Tianjin-Hebei region also showed a decreasing trend, but the range of this change was larger than that under RCP 4.5. Some GCMs predicted an increase, while others predicted a decrease (Table 3). On the whole, the average maximum number of consecutive days without precipitation in the Beijing-Tianjin-Hebei region is expected to decrease. Consequently, the drought risk in this area would slightly decrease. This may be considered a good thing, despite the very small change projected. The Beijing-Tianjin-Hebei Urban Agglomeration belongs to an arid and a semi-arid climate zone and the shortage of water resources is one of the main factors restricting urban development. Unfortunately, the regional average annual Pxcdd in the Xiongan New Area, which is under construction, is expected to increase in the future under both scenarios ( Table 4). The risk of drought disaster in this area may further increase. Figure 8 shows the spatial distribution of the percentage changes of Pxcdd across the Beijing-Tianjin-Hebei region during the three periods compared with 1961-2005. The increasing magnitude in some stations was more than 30%, while in some other stations, the decreasing magnitude was less than −30%.

Conclusions
Future precipitation at the local scale in China's Beijing-Tianjin-Hebei region was

Conclusions
Future precipitation at the local scale in China's Beijing-Tianjin-Hebei region was projected in this study using a statistical downscaling method with the weather generator BCC/RCG-WG driven by the output of five GCMs. Daily precipitation was simulated for the three periods 2006-2030, 2031-2050, and 2051-2070 at 174 stations in the Beijing-Tianjin-Hebei region. The large-scale climate change signal for the simulations of the future daily local precipitation climate was taken from the five GCMs under the RCP 4.5 and RCP 8.5 emission scenarios. One important objective of this study was to quantify future mean and daily extreme precipitation at the station scale in the Beijing-Tianjin-Hebei region. This was achieved by using eight indices for precipitation. These indices were derived from the simulated daily precipitation at each of the 174 stations during the three periods. The results showed a general trend toward wetter and stronger extremes across the Beijing-Tianjin-Hebei region on an annual scale in the future, and are generally consistent with the large-scale signal from the GCMs. However, the downscaled scenarios showed local-scale variations that could not be captured by the GCMs.
Specifically, the following conclusions can be drawn from this work: • Compared with the baseline during 1961-2005, the regional average annual precipitation is projected to increase in all three future periods across the Beijing-Tianjin-Hebei region under both RCP 4.5 and RCP 8.5. This increase would be particularly large during 2051-2070 under RCP 8.5, with an increase in 10.3%.

•
The projected changes in the number of days with precipitation are relatively small across the Beijing-Tianjin-Hebei region. Regional average Nrain increases by 0.2~1.0% under both RCP 4.5 and RCP 8.5, except during 2031-2050 under RCP 8.5 when it decreases by 0.7%.

•
The annual intensity of precipitation at most stations across the Beijing-Tianjin-Hebei region is projected to increase except for some southern stations. Especially in 2051-2070 under RCP 8.5, the intensity would increase by more than 10% at most northern stations.

•
There would be more extreme precipitation events across the Beijing-Tianjin-Hebei region in the future. Under RCP 4.5, Exc25 would continually increase across the Beijing-Tianjin-Hebei region. The regional average is projected to increase by 14.8%, 15.0%, and 15.3% during 2006-2030, 2031-2050, and 2051-2070, respectively. In 2051-2070 under RCP 8.5, this change reaches 18.9%. At most northern stations, Exc25 is projected to increase by more than 20%, and even more than 50% at some northwest stations.

•
The relative increase in Exc40 is the largest among the eight indices. The regional average annual Exc40 across the Beijing-Tianjin-Hebei region would increase by more than 20% under RCP 4.5. Under RCP 8.5 during 2051-2070, this increase would reach 26.3%.

•
Although simulated Px1d and Px5d increase overall in the Beijing-Tianjin-Hebei region, there is high variability among stations, which demonstrates the need for downscaling. These indices would increase at some stations, and decrease at others. The regional average of Px1d and Px5d across the Beijing-Tianjin-Hebei region simulated by the five GCMs presented an increase of 8~15% under both RCP 4.5 and RCP 8.5, and there would be decreases in these indices at 20~35% of the stations in the region. • Generally, the average Pxcdd in the Beijing-Tianjin-Hebei region is projected to decrease in the future, and the drought risk in this area is expected to decrease. This change may be considered a good thing, despite the very small change projected. However, the consistency of change in Pxcdd between stations is the lowest among the eight indices, and around half of the stations showed negative changes under both RCP 4.5 and RCP 8.5 in the future three periods.