Human migration

Weather information: countries where there are weather forecasts available. Pixels with a value higher than the specified threshold (50%) were given a value of -1 (YES response) Official weather observations, weather forecasts and climatological information for selected cities supplied by National Meteorological & Hydrological Services (NMHSs) worldwide. The NMHSs make official weather observations in their respective countries. Links to their official weather service websites and tourism board/organization are also provided whenever available. Weather icons are shown alongside worded forecasts in this version to facilitate visual inspection. By June 2021, WWIS provided official weather information for 2963 cities in which 2819 cities are available with forecast from 139 members while 2177 cities are available with climatological information from 170 members. **Data revision:** 2021-07-01 **Contact points:** Contact: [WMO](mailto:webmaster@worldweather.org) Metadata contact: [OCB Environment](mailto:OCB-Environment-List@fao.org) FAO-UN **Resource constraints:** License CC BY-NC-ND 4.0: Attribution-NonCommercial-NoDerivatives 4.0 International Notes to users of the forecast data and climatological information of the WWIS website: 1. Acknowledgement must be given to the WMO World Weather Information Service (https://worldweather.wmo.int) as the source of information. 2. The forecast and climatological information of the WWIS website must be reproduced accurately. 3. Disclaimer: https://worldweather.wmo.int/en/disclaimer.html **Online resources:** Download data from World Weather Information Service Official Forecasts

Epidemics: countries where humanitarian crises, including health crises, are impeding the population to address the potential impacts of climate change. Pixels with a value higher than the specified threshold (6) were given a value of 1 (YES response) The INFORM Epidemic Risk Index is a prototype version of hazard dependent INFORM Risk Index created in 2018. It was developed under the technical lead of the JRC and in close collaboration with WHO for the epidemic components. In its original version it covers four groups of Infectious diseases on the base of the mode of transmission and the epidemiological triad addressing agent, host and environment: Zoonoses, Vector borne, Person-to-person (P2P), Foodborne and Waterborne. **Data revision:** 2020-01-01 **Contact points:** Contact: [DRMKC - Disaster Risk Management Knowledge Centre](mailto:EC-drmkc@ec.europa.eu) JRC European Commission Metadata contact: [OCB Environment](mailto:OCB-Environment-List@fao.org) FAO-UN **Resource constraints:** license **Online resources:** The INFORM Epidemic Risk Index assesses the risk of countries to epidemic outbreak, which would exceed the national capacity to respond to the crisis.

This climate projection dataset contains global, daily gridded data for the SSP585 2050 scenario to be used in the identification and monitoring of hydroclimatic extremes. The Climate Hazards Center Coupled Model Intercomparison Project Phase 6 climate projection dataset (CHC-CMIP6) was developed to support the analysis of climate-related hazards, including extreme heat conditions, over the recent past and in the near-future. Global daily high resolution (0.05°) grids of the Climate Hazards InfraRed Temperature with Stations temperature product form the basis of the 1983–2016 historical record. Large CMIP6 ensembles from the Shared Socioeconomic Pathway 2-4.5 and SSP 5-8.5 scenarios were then used to develop high resolution daily 2030 and 2050 ‘delta’ fields. These deltas were used to perturb the historical observations, thereby generating 0.05° 2030 and 2050 temperature projections. Finally, monthly counts of frequency of extremes for each variable were derived for each time period. Two scenarios were used from CMIP6—Shared Socioeconomic Pathway (SSP) 2–4.5 and 5–8.537. The SSP245 scenario is based on ‘middle-of-the-road’ projections of development (SSP2). The SSP585 scenario projects rapid fossil fuel development and increased global market integration (SSP5). These are generally considered the most-likely scenario (SSP245) and the high-emissions scenario (SSP585) Given the two projection periods 2025–2035 and 2045–2055, projections for four CMIP6 scenarios (2030_SSP245, 2030_SSP585, 2050_SSP245, 2050_SSP585) were derived. Counts of the number of extreme days per month were calculated for Tmax for the four scenarios. Definitions of extremes for each variable were based on two methods: known thresholds (30°C and 40.6°C) and by calculating pixel-specific breakpoints using the 95th and 99th percentile. 30°C and 40.6°C represent moderate and extreme heat exposure. These were chosen based on documented thresholds for agricultural and human heat stress. For each variable, year, and scenario, the number of days surpassing each variables’ thresholds were calculated. For Tmax for each pixel, the daily 95th and 99th percentiles were calculated using 1983–2016 daily data, resulting in a 95th and 99th percentile value for each variable. For each of these variables, each year (1983–2016), and each of the four scenarios, the number of days for each month were calculated at each pixel that surpass these percentile-defined extreme values. More information can be found in this article in the Nature journal and and in this technical documentation.

This climate projection dataset contains global, daily gridded data for the SSP585 2030 scenario to be used in the identification and monitoring of hydroclimatic extremes. The Climate Hazards Center Coupled Model Intercomparison Project Phase 6 climate projection dataset (CHC-CMIP6) was developed to support the analysis of climate-related hazards, including extreme heat conditions, over the recent past and in the near-future. Global daily high resolution (0.05°) grids of the Climate Hazards InfraRed Temperature with Stations temperature product form the basis of the 1983–2016 historical record. Large CMIP6 ensembles from the Shared Socioeconomic Pathway 2-4.5 and SSP 5-8.5 scenarios were then used to develop high resolution daily 2030 and 2050 ‘delta’ fields. These deltas were used to perturb the historical observations, thereby generating 0.05° 2030 and 2050 temperature projections. Finally, monthly counts of frequency of extremes for each variable were derived for each time period. Two scenarios were used from CMIP6—Shared Socioeconomic Pathway (SSP) 2–4.5 and 5–8.537. The SSP245 scenario is based on ‘middle-of-the-road’ projections of development (SSP2). The SSP585 scenario projects rapid fossil fuel development and increased global market integration (SSP5). These are generally considered the most-likely scenario (SSP245) and the high-emissions scenario (SSP585) Given the two projection periods 2025–2035 and 2045–2055, projections for four CMIP6 scenarios (2030_SSP245, 2030_SSP585, 2050_SSP245, 2050_SSP585) were derived. Counts of the number of extreme days per month were calculated for Tmax for the four scenarios. Definitions of extremes for each variable were based on two methods: known thresholds (30°C and 40.6°C) and by calculating pixel-specific breakpoints using the 95th and 99th percentile. 30°C and 40.6°C represent moderate and extreme heat exposure. These were chosen based on documented thresholds for agricultural and human heat stress. For each variable, year, and scenario, the number of days surpassing each variables’ thresholds were calculated. For Tmax for each pixel, the daily 95th and 99th percentiles were calculated using 1983–2016 daily data, resulting in a 95th and 99th percentile value for each variable. For each of these variables, each year (1983–2016), and each of the four scenarios, the number of days for each month were calculated at each pixel that surpass these percentile-defined extreme values. More information can be found in this article in the Nature journal and and in this technical documentation.

Extreme precipitation: areas where maximum 1-day precipitation is above 50mm on average per year. Pixels with a value higher than the specified threshold (50mm) were given a value of 1 (YES response) Historical climate data are an ensemble of outputs from CMIP6 earth system models for the time period 1981-2010 **Data publication:** 2022-01-26 **Citation:** Citation: Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell & Joel Michaelsen. "The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes". Scientific Data 2, 150066. doi:10.1038/sdata.2015.66 2015. **Contact points:** Metadata Contact: [OCB Environment](mailto:OCB-Environment-List@fao.org) **Online resources:**

This climate projection dataset contains global, daily gridded data for the SSP245 2050 scenario to be used in the identification and monitoring of hydroclimatic extremes. The Climate Hazards Center Coupled Model Intercomparison Project Phase 6 climate projection dataset (CHC-CMIP6) was developed to support the analysis of climate-related hazards, including extreme heat conditions, over the recent past and in the near-future. Global daily high resolution (0.05°) grids of the Climate Hazards InfraRed Temperature with Stations temperature product form the basis of the 1983–2016 historical record. Large CMIP6 ensembles from the Shared Socioeconomic Pathway 2-4.5 and SSP 5-8.5 scenarios were then used to develop high resolution daily 2030 and 2050 ‘delta’ fields. These deltas were used to perturb the historical observations, thereby generating 0.05° 2030 and 2050 temperature projections. Finally, monthly counts of frequency of extremes for each variable were derived for each time period. Two scenarios were used from CMIP6—Shared Socioeconomic Pathway (SSP) 2–4.5 and 5–8.537. The SSP245 scenario is based on ‘middle-of-the-road’ projections of development (SSP2). The SSP585 scenario projects rapid fossil fuel development and increased global market integration (SSP5). These are generally considered the most-likely scenario (SSP245) and the high-emissions scenario (SSP585) Given the two projection periods 2025–2035 and 2045–2055, projections for four CMIP6 scenarios (2030_SSP245, 2030_SSP585, 2050_SSP245, 2050_SSP585) were derived. Counts of the number of extreme days per month were calculated for Tmax for the four scenarios. Definitions of extremes for each variable were based on two methods: known thresholds (30°C and 40.6°C) and by calculating pixel-specific breakpoints using the 95th and 99th percentile. 30°C and 40.6°C represent moderate and extreme heat exposure. These were chosen based on documented thresholds for agricultural and human heat stress. For each variable, year, and scenario, the number of days surpassing each variables’ thresholds were calculated. For Tmax for each pixel, the daily 95th and 99th percentiles were calculated using 1983–2016 daily data, resulting in a 95th and 99th percentile value for each variable. For each of these variables, each year (1983–2016), and each of the four scenarios, the number of days for each month were calculated at each pixel that surpass these percentile-defined extreme values. More information can be found in this article in the Nature journal and and in this technical documentation.