德国与印度对比

德国与印度：温度

2025 年温度：德国为 10.1 °C，印度为 24.3 °C（据 ERA5 观测）。 2025 年印度的数值较高，高出 14.2 °C，是德国的 2.4 倍。

在地图上查看其变化

°C · ERA5

德国与印度：温度2025 年温度：德国为 10.1 °C，印度为 24.3 °C（据 ERA5 观测）。 2025 年印度的数值较高，高出 14.2 °C，是德国的 2.4 倍。
德国印度CMIP6 历史模拟，模型中位数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。
  • 2025 年，在 233 个国家中，德国排名第 200，印度排名第 106（从高到低）。
  • 根据CMIP6 模型中位数（SSP2-4.5，中间路径），2100 年德国为 11.8 °C，印度为 26.6 °C。

逐年数据

°C · ERA5

年份德国德国：CMIP6 历史模拟，模型中位数印度印度：CMIP6 历史模拟，模型中位数
202510.110.124.324.8
2024119.924.824.9
202310.81024.524.9
202210.710.424.524.8
20219.310.124.324.8
202010.61024.224.7
201910.49.824.524.7
201810.79.824.524.7
20179.89.624.724.7
20169.79.724.924.6
201510.19.724.524.7
201410.59.524.224.6
20138.99.62424.6
20129.39.624.224.7
20119.89.624.124.6
201089.624.724.6
20099.49.724.824.5
20089.79.82424.4
200710.19.524.324.4
20069.89.524.424.4
20059.29.624.124.5
20049.19.224.224.5
20039.59.424.224.4
20029.69.224.424.5
20019.19.224.124.5
20009.99.32424.4
19999.6924.124.4
19989.19.124.224.3
199798.923.624.3
19967.29.12424.2
199598.724.124.3
19949.88.923.924.1
19938.58.52424
19929.38.423.823.9
19918.48.823.924.3
19909.58.923.724.3
19899.6923.724.3
19889.18.724.224.2
19877.58.724.324.2
198688.423.924.2
19857.58.824.124.1
198488.423.824.1
19839.18.423.624
198298.423.824.1
19818.48.423.824.2
19807.88.524.224.1
19797.98.524.324.1
197888.623.724
19778.98.323.924.1
19768.78.323.924.1
19759.18.323.523.9
19749.18.623.924.1
19738.58.424.124.2
19728.18.224.124
19718.88.323.424.1
197088.123.824
196988.224.324
19688.48.323.624
19679.28.323.824
19668.88.223.824
19657.88.323.824
19648.48.223.823.9
19637.48.523.823.9
19627.58.323.424.1
19619.28.423.324.1
19608.78.42424.1
19599.28.523.924.2
19588.48.424.224.1
19578.78.223.824.1
19566.98.623.524.1
19557.68.523.624.1
19547.88.223.724.1
19539.18.322.624.1
195288.62324.1
19518.98.422.924.2
19508.88.522.824.2
  • 12.3预测值
CMIP6 模型中位数（SSP2-4.5，中间路径） · 2026–2100
年份德国德国：CMIP6 历史模拟，模型中位数印度印度：CMIP6 历史模拟，模型中位数
2026–10.49.4–11.3–24.924–25.6
2027–10.29.5–11.1–24.924.1–25.7
2028–10.39.5–11.4–24.824.3–25.5
2029–10.29.4–11.3–25.124.4–25.8
2030–10.19.2–11.2–24.924.2–25.7
2031–10.18.9–11.6–2524.3–25.7
2032–10.69.1–11.3–25.124.3–25.8
2033–10.29.5–11.5–2524.3–25.6
2034–10.59.4–11.6–25.124.4–25.9
2035–10.49.2–11.5–25.124.4–25.9
2036–10.69.5–11.9–25.124.4–25.7
2037–10.39.3–11.6–25.324.6–26
2038–10.39.5–11.3–25.324.5–26
2039–10.49.3–11.4–25.224.5–25.9
2040–10.49.4–11.8–25.124.4–26.1
2041–10.39.5–11.3–25.324.7–26
2042–10.79.5–11.4–25.524.7–26.2
2043–10.69.3–11.7–25.324.6–26.1
2044–10.49.2–11.9–25.424.7–26.2
2045–10.59.6–11.6–25.424.7–26.1
2046–10.39.4–11.4–25.624.6–26.5
2047–10.89.8–11.6–25.624.7–26.4
2048–1110.1–12–25.724.8–26.6
2049–10.79.7–12.2–25.624.9–26.6
2050–10.910–11.9–25.724.9–26.7
2051–10.79.3–12.2–25.725–26.6
2052–10.99.3–12.4–25.724.9–26.5
2053–10.99.7–11.9–25.725–26.6
2054–10.89.7–11.9–25.824.9–26.7
2055–10.89.6–11.9–25.825.1–26.7
2056–10.99.7–12.4–25.925–26.8
2057–10.89.9–12.5–25.924.9–26.8
2058–11.310.1–12.5–25.825.1–26.9
2059–10.99.6–12.2–2625.2–27
2060–11.210–12–26.125.3–26.8
2061–11.210.3–11.9–2625.1–26.8
2062–11.210.3–12.3–25.925.1–27.2
2063–11.210–12.5–25.925.2–27
2064–10.910–11.8–2625.2–27
2065–119.7–12.3–26.125.3–27.1
2066–11.29.7–12.2–26.125.4–27
2067–11.410.1–12.4–25.925.2–27.2
2068–11.210.1–12.5–26.125.2–27.3
2069–11.510–12.6–26.225.3–27.2
2070–11.19.9–12.9–2625.3–27.1
2071–11.39.6–12.8–26.125.3–27.3
2072–11.210.3–12.5–26.225.4–27.1
2073–11.310.3–12.2–26.325.5–27.1
2074–11.410.2–12.6–26.225.5–27.2
2075–10.89.9–12.6–26.225.4–27.1
2076–11.210.3–13.1–26.225.4–27.4
2077–11.110.1–12.6–26.325.5–27.4
2078–11.210.2–13–26.325.5–27.3
2079–11.210.1–12.8–26.325.4–27.2
2080–11.210.3–12.6–26.425.5–27.4
2081–11.510.2–12.9–26.425.7–27.5
2082–11.410.3–12.9–26.325.6–27.5
2083–11.210.1–12.7–26.525.7–27.6
2084–11.510.2–12.7–26.525.5–27.6
2085–11.610.5–12.9–26.525.5–27.8
2086–11.710.4–13.1–26.425.4–27.7
2087–11.610.3–13.2–26.425.4–27.6
2088–11.710.5–13–26.425.5–27.8
2089–11.710.5–12.7–26.425.6–27.6
2090–11.810.1–13–26.525.7–27.6
2091–11.710.2–13.1–26.625.7–27.8
2092–11.810.2–13–26.625.7–27.7
2093–11.510–13–26.525.6–27.8
2094–11.510.2–13.2–26.625.7–27.7
2095–11.510.2–13–26.825.7–27.7
2096–11.810–13.1–26.525.6–27.8
2097–11.510.4–13.2–26.525.6–27.6
2098–11.610.6–13–26.725.7–27.7
2099–1210.4–12.7–26.725.7–27.8
2100–11.810.4–12.8–26.625.8–27.7

分别查看各国

须知

  • 全国范围、全年平均的近地面气温。
  • 来源：ERA5 reanalysis (Copernicus, ECMWF) and CMIP6, via the World Bank Climate Change Knowledge Portal。
  • 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。
  • 排名基于 2025 年有数值的 233 个国家，从高到低排列。

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