中国与印度：热带夜

2014 年热带夜：中国为 41.5 夜，印度为 215.5 夜（CMIP6 气候模型中位数模拟）。 2014 年印度的数值较高，高出 174.1 夜，是中国的 5.2 倍。

每年夜数 · CMIP6

中国印度CMIP6 模型中位数（SSP2-4.5，中间路径）模型第 10–90 百分位

数据解读

自 1950 年以来，在两国都有数据的每一年，印度的数值都更高。
根据CMIP6 模型中位数（SSP2-4.5，中间路径），2100 年中国为 63.3 夜，印度为 250.4 夜。

逐年数据

每年夜数 · CMIP6

年份	中国	印度
2014	41.5	215.5
2013	41.7	216
2012	40.8	217.4
2011	40.2	215.5
2010	40.5	215.2
2009	39.5	213.7
2008	39.2	213.7
2007	38.7	213.3
2006	39.5	212.5
2005	39.5	212.9
2004	39.7	214.9
2003	38.5	212.1
2002	38.8	213
2001	39.5	212.2
2000	38.6	210.8
1999	38.5	211.9
1998	37.6	210.3
1997	37.1	209.5
1996	36.6	207
1995	35.1	208.8
1994	34.9	207.1
1993	33.2	206
1992	32.3	203.5
1991	35.9	208
1990	36.4	209.9
1989	36.1	208
1988	35.1	207.3
1987	34.5	207.2
1986	34	207.2
1985	33.5	205.9
1984	33.4	206.7
1983	31.5	203.3
1982	34.1	205.7
1981	34.2	208.4
1980	34.3	205.3
1979	33.8	205.5
1978	34.8	204.1
1977	33.3	205.9
1976	32.7	204.6
1975	33.1	202.6
1974	33.3	205.3
1973	33.1	205.6
1972	32.6	203.8
1971	33	203.6
1970	32.4	202.6
1969	33.4	203.6
1968	32.8	201.7
1967	33.4	201.6
1966	33.4	203.9
1965	32.5	202.4
1964	32.1	201.2
1963	34.5	201.8
1962	34	203.8
1961	34.6	206.3
1960	34.1	206
1959	34	206.1
1958	35	204
1957	34.4	206.5
1956	34.6	204
1955	34.2	203.2
1954	34.2	206
1953	34.3	204
1952	34.3	204.4
1951	35.1	204.3
1950	34.9	206.3

CMIP6 模型中位数（SSP2-4.5，中间路径） · 2015–2100

年份	中国	印度
2015	42.334.8–49.8	216.9202.1–228.4
2016	42.134.4–50	215202.5–227.6
2017	43.135.3–51.6	216.2202.1–228.5
2018	42.335.5–49.5	218.3204.5–231.4
2019	43.836.5–51.1	217.4202.3–230.4
2020	43.736.8–51.1	217.6203.1–232
2021	44.136–52.1	219.7204.5–233.2
2022	44.737.5–52.7	220.5205.8–233
2023	45.737.8–53.4	220.4206.4–233.7
2024	45.738.1–54	222.6208.1–236.3
2025	46.337.5–54.8	219.7202.7–236.2
2026	46.237.6–55.5	220.2204.2–236.8
2027	46.138–54.9	223.6207.1–239
2028	46.438.4–54.3	222.1205.1–235.7
2029	46.939.1–55.6	225.4210–240.3
2030	47.138.8–55.3	222.7207.3–235.6
2031	4738.8–56	223.8207.1–237.8
2032	48.339.3–57.4	225.9210–240.5
2033	47.939.7–56.6	223.8209.3–235.4
2034	48.440.5–57.3	225.8210–242.7
2035	49.740.8–58.6	224.9210.6–240.9
2036	4940.1–58.2	224.2210.4–240.3
2037	4941.2–57.6	228.3213.6–242.6
2038	50.542.4–59.3	228.1212.6–241.4
2039	49.641.2–59.2	227.4211.9–242.8
2040	50.942.4–60.4	227212.2–242
2041	51.342.5–61.1	227.1210.7–240.5
2042	51.642–61.5	229.1214.8–244.2
2043	50.742.7–61	228.9214.5–244
2044	51.942.4–61.9	229.7214.7–244.2
2045	51.542.8–61.1	231.2216.6–246.1
2046	5343.7–62.7	232.6216.2–248.6
2047	53.544.5–62.5	232.4215.5–250
2048	53.144–62.6	235.6218.5–254.3
2049	52.843.8–63.3	235.3218.3–249.9
2050	53.644.4–63.8	233.5216.6–253.4
2051	54.345.1–64.8	234.9218.6–249.9
2052	53.444.6–63.2	234219.4–249.3
2053	54.244.2–64.2	234.9219.5–252.5
2054	54.545.3–64.8	237.2219.4–253.1
2055	5545.1–65.8	237.2220.7–256.6
2056	55.245.1–66.6	238.8221.2–254.9
2057	54.844.9–65.7	235.7218.5–253.8
2058	55.145.8–67.7	238.8222.8–254.1
2059	55.345.1–67.2	241.1224.9–256.2
2060	57.246.9–68.1	240.9225.6–255.6
2061	56.546.6–66.3	237.9220.6–253.7
2062	56.846.9–68.5	239.9221.2–259.7
2063	57.247.2–68.6	239.9222.3–256.1
2064	5747.1–67.7	240223.8–256.3
2065	57.347.1–67.8	240.9222.9–257.3
2066	57.347.9–68.7	241.5224.1–258.6
2067	57.546.4–68.2	241.6224.8–257.9
2068	58.748.5–70.5	242.3224.6–262.1
2069	58.548.4–69	242.8225.4–259.1
2070	5847.6–71.7	240.1225.3–257.1
2071	59.347.4–71.6	241.8223.7–260.2
2072	59.248.1–71.5	242.6225.4–259.2
2073	59.849.1–70.9	243.8228–259.3
2074	59.648.7–71.1	241.6226.9–258.4
2075	58.748.3–71.4	243.5226.4–258.2
2076	60.249–70.9	243.2224.6–259.9
2077	60.851.1–72.4	243.7226.6–264.6
2078	59.648.8–72.4	246.7230.1–261.5
2079	60.750.1–72.6	244.9226.4–261.3
2080	59.950–72.3	246.7227–263.7
2081	60.950–73.2	247.5230.4–264.8
2082	6149.8–73.3	244.6227.9–262.3
2083	61.251.2–73.6	247.5227.8–265.1
2084	61.749.7–74.6	247.3228.3–266.8
2085	61.250.8–75.2	246.5227.5–267.1
2086	61.648.9–74.5	245.6227.7–266.5
2087	61.650.6–75	245.5226.7–266.2
2088	62.750.5–77.1	247229.4–266.9
2089	61.950–75.2	248.1230–266.3
2090	62.551.4–73.7	249228.9–266.4
2091	63.852–75.7	248.9230–266.5
2092	63.251.2–75.1	249.8232.3–267.8
2093	63.252–75.4	248.1229–266.5
2094	6351.1–76.3	249.1230.6–268.4
2095	63.952.8–76.4	251233.2–270
2096	63.852.2–76.8	247.8231.4–269.3
2097	62.750.7–75.1	251.1231.4–268
2098	63.451.2–76.8	252.3230.8–268.3
2099	63.851.9–76.1	248.7230.1–268.3
2100	63.351.7–76.2	250.4232.9–266.7

分别查看各国

须知

一年中气温始终高于 20 °C 的夜晚数，取全国平均。
来源：World Bank Climate Change Knowledge Portal, CMIP6 multi-model median。
虚线为CMIP6 模型中位数（SSP2-4.5，中间路径），是一种情景而非预报；阴影带为其模型第 10–90 百分位。
气候模型值为 CMIP6 各模型的中位数：2014 年及以前按历史排放模拟，此后按中间路径 SSP2-4.5 模拟；它们并非实测数据。

各图层如何运作、数据从何而来，以及它们无法告诉你什么：历年各国数据

中国与印度对比

健康

人口与社会

经济

教育

政治

冲突

冲突死亡人数中国：2022 年为 0 人死亡印度：2022 年为 358 人死亡