Bangladesh vs Thailand

Bangladesh vs Thailand: Days above 35 °C

Days above 35 °C in 2014: 19.4 days in Bangladesh and 39.9 days in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 20.5 days, or 2.06 times that in Bangladesh.

days a year · CMIP6

Bangladesh vs Thailand: Days above 35 °CDays above 35 °C in 2014: 19.4 days in Bangladesh and 39.9 days in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 20.5 days, or 2.06 times that in Bangladesh.
BangladeshThailandCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Thailand has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Bangladesh at 56.8 days and Thailand at 95.9 days in 2100.

Every year

days a year · CMIP6

YearBangladeshThailand
201419.439.9
201314.142.1
20121740.1
201113.439.9
201016.939.8
200914.437.9
20081436
200714.333
200617.839.2
200516.134.9
200414.739.4
200315.934.1
200217.736.3
200116.937.8
200016.632.5
199915.629.7
199815.433.3
199716.833.7
199614.231.3
199513.233.5
199415.233.5
199312.430.4
1992825.5
199118.835.4
199018.537.7
198913.729.3
198813.530.6
198717.931.2
198617.934.3
198514.429.3
19841932.4
198314.630.4
198216.333.1
198115.429.5
198014.528.8
197916.732.4
19781526.1
197714.131.5
197617.529.7
197514.425
197417.331.5
19731734.5
197215.730.3
197116.927.1
197016.528.7
196915.529.9
196816.229.4
196713.528.3
196615.625.4
196515.427.1
196414.726.6
196316.126
196219.128.7
19611831.1
196019.232.9
19591824.4
195817.533
195718.127.8
195619.227.1
195516.931.2
195419.430.3
19532128.2
195217.234.1
195119.933
195022.328.5
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearBangladeshThailand
201517.85.5–30.241.123.5–66.3
201617.53.9–31.538.915.7–61.2
201714.14.8–2941.420.8–64.9
201813.92–3143.521.7–65.5
201914.93.9–2840.916.5–61.9
202014.94.1–36.345.617.3–66.6
202117.25.6–3343.720.7–70.7
202217.21.4–36.647.826.4–73.3
202318.16.6–32.241.822.3–67.2
202421.24.9–36.644.726.9–70.9
202516.73.9–36.850.227.3–77
202618.36.7–3444.425.2–80.5
202718.33.5–38.247.126.2–75.6
202818.64.2–33.251.123.1–73.3
202918.22.2–34.956.733.9–84.7
2030174.6–31.350.726.1–78.9
2031153.9–32.150.531.5–81.2
203219.33.2–34.349.823.6–85.7
203317.53.9–3253.432–73.7
203416.24–29.351.531.6–83.4
203516.26.6–31.652.228.9–89.2
203617.76.3–36.653.335.2–77.4
203720.65.7–39.157.632–82.5
203821.96.6–38.562.238.5–91.5
203921.45.1–43.255.127.8–78.8
204022.86–48.853.728.4–91.3
204119.44.7–44.56337.5–86.6
204222.86.3–50.654.826.4–94.7
204325.17.2–4757.129.8–85.3
204424.77.9–4557.336.7–95.7
204524.110–42.554.934.2–90.2
204628.57.8–55.762.736.4–102.1
204726.47.4–50.663.635–98.5
204830.28–52.972.237.4–110.4
204929.511.3–54.166.240.8–98.3
205026.810.4–47.864.536.9–95.3
205132.713.4–54.366.135.5–97.1
205229.313–59.874.339.3–108.1
205326.79.4–54.964.635–106.7
205429.910.9–57.769.144.2–112.1
205534.613.5–60.871.445.3–107.6
205634.215.2–5970.536.5–117.7
20573912.3–72.87240–104.6
205838.917–69.973.140.6–107.3
205935.914.6–6677.251.2–121.4
206036.818.6–65.380.946–117
206138.919.6–65.977.335.3–117.8
206235.115.8–63.873.535.5–125.8
206342.114.5–66.983.640.3–122.6
206437.317–62.774.746.9–111.8
206535.618.5–60.269.939.5–124.8
206635.812.3–64.780.250.7–122.7
206732.612.9–61.869.437.9–116
206840.717.5–76.476.445.8–131.7
206943.915.7–73.377.747.4–128.7
207033.714.7–6373.839.2–129.5
207142.817.7–73.980.642.2–131.6
207242.718.2–68.181.347.5–135.3
207344.423.8–82.388.854.2–133.4
20744214.1–71.68444.3–128.1
207541.515.2–70.182.838.3–123.9
207639.914.1–81.282.937.1–132.9
207742.723.6–83.68849.4–132.4
207844.424.7–79.291.252–137
207943.923.1–74.785.749.5–144.4
208052.224.5–8388.552.7–143.6
208150.628.6–8387.150.9–135.1
208249.322.9–92.189.953.5–133.6
208349.218.9–88.787.145.8–149.7
208450.421.6–92.68447.7–131
208549.920.5–104.593.853.8–141.8
208650.922.5–100.687.745.8–139.2
208753.125–82.186.447.4–139.2
208846.314.7–108.490.441.7–153.2
208952.722.8–107.690.149–151.2
209055.216.3–89.287.853.3–154.1
209151.331.2–988648.2–148.6
209255.422.1–101.292.650.2–149.9
209347.718.8–94.689.854.6–159
209451.225.3–87.395.549.6–143.6
209557.624.4–95.1103.458.2–156.3
209650.820–91.889.951.3–155.4
209748.923.7–9694.550.2–142.6
209852.926.4–94.789.851.6–149.6
209948.722.9–8288.244.7–147.4
210056.822.9–95.895.951.1–153.3

Each country on its own

Good to know

  • Days in a year whose highest temperature passes 35 °C, averaged over the country.
  • Source: World Bank Climate Change Knowledge Portal, CMIP6 multi-model median.
  • The dashed line is the CMIP6 model median (SSP2-4.5, middle of the road), a scenario rather than a forecast; the band is its models’ 10th–90th percentile.
  • Climate model values are the median of the CMIP6 models, run on past emissions to 2014 and on SSP2-4.5, a middle-of-the-road path, after: they are not measurements.

How the layers work, where their data comes from and what they can’t tell you: Countries through time

Bangladesh vs Thailand

Health

People

Economy

Education

Politics

Conflict

Military

Climate

Technology and environment