This statistic shows the projected top ten largest national economies in 2050. By 2050, China is forecasted to have a gross domestic product of over ** trillion U.S. dollars.

The impact of climate change has been forecasted to affect the economies of South-East Asian Nations (ASEAN) the hardest. The maximum projected loss incurred by the ASEAN in the event of a 3.2°C temperature rise is 37.4 percent. This is more than double the forecast loss of the Advanced Asia economies and 10 percent higher than the next largest forecast loss of the Middle East & Africa.

This statistic shows the top ten countries projected to have the greatest average annual growth in gross domestic product from 2016 to 2050. From 2016 to 2050, Vietnam is projected to have an average annual GDP growth rate of * percent.

From the IPCC website: The B2 world is one of increased concern for environmental and social sustainability. Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median projections. Income per capita grows at an intermediary rate to reach about US$12,000 by 2050. By 2100 the global economy might expand to reach some US$250 trillion. International income differences decrease, although not as rapidly as in scenarios of higher global convergence (A1, B1). Local inequity is reduced considerably through the development of stronger community support networks. Generally high educational levels promote both development and environmental protection. Indeed, environmental protection is one of the few remaining truly international priorities. However, strategies to address global environmental challenges are less successful than in B1, as governments have difficulty designing and implementing agreements that combine environmental protection with mutual economic benefits. The B2 storyline presents a particularly favorable climate for community initiative and social innovation, especially in view of high educational levels. Technological frontiers are pushed less than in A1 and B1 and innovations are also regionally more heterogeneous. Globally, investment in R&D continues its current declining trend, and mechanisms for international diffusion of technology and know-how remain weaker than in scenarios A1 and B1 (but higher than in scenario A2). Some regions with rapid economic development and limited natural resources place particular emphasis on technology development and bilateral co-operation. Technical change is therefore uneven. The energy intensity of GDP declines at about one percent per year, in line with the average historical experience of the last two centuries. Land-use management becomes better integrated at the local level in the B2 world. Urban and transport infrastructure is a particular focus of community innovation, contributing to a low level of car dependence and less urban sprawl. An emphasis on food self-reliance contributes to a shift in dietary patterns towards local products, with reduced meat consumption in countries with high population densities. Energy systems differ from region to region, depending on the availability of natural resources. The need to use energy and other resources more efficiently spurs the development of less carbon-intensive technology in some regions. Environment policy cooperation at the regional level leads to success in the management of some transboundary environmental problems, such as acidification due to SO2, especially to sustain regional self-reliance in agricultural production. Regional cooperation also results in lower emissions of NOx and VOCs, reducing the incidence of elevated tropospheric ozone levels. Although globally the energy system remains predominantly hydrocarbon-based to 2100, there is a gradual transition away from the current share of fossil resources in world energy supply, with a corresponding reduction in carbon intensity. Data are available for the following periods: 1961-1990, 2010-2039; 2040-2069; and 2090-2099 Mean monthly and change fields.

1. Temporal Coverage of Data: The data collection periods are 2010, 2030, and 2050.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope: The data pertains to the fields of Earth Sciences and Geography.4. Data Volume: The total data volume is approximately 31.5 MB.5. Data Type: Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene): · 7. Field (Feature) Name Explanation:a. Name Explanation: IND: Industrial Value Addedb. Unit of Measurement: Unit: US Dollars (USD)8. Data Source Description:a. Remote Sensing Data:2010 Global Vegetation Index data (Enhanced Vegetation Index, EVI, from MODIS monthly average data) and 2010 Nighttime Light Remote Sensing data (DMSP/OLS)b. Meteorological Data:From the CMCC-CM model in the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC)c. Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agenciesd. Gross Domestic Product Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencese. Other Data:Rivers, roads, settlements, and DEM, sourced from the National Oceanic and Atmospheric Administration (NOAA), Global Risk Data Platform, and Natural Earth9. Data Processing Methods(1) Spatialization of Baseline Industrial Value Added: Using 2010 global EVI vegetation index data and nighttime light remote sensing data, we addressed the oversaturation issue in nighttime light data by constructing an adjusted nighttime light index to obtain the optimal global light data. The EANTIL model was developed using NTL, NTLn, and EVI data, with the following formula:Here, EANTLI represents the adjusted nighttime light index, NTL represents the original nighttime light intensity value, and NTLn represents the normalized nighttime light intensity value. Based on the optimal light index EANTLI and the industrial value-added data from the World Bank, we constructed a regression allocation model to derive industrial value added (I), generating the global 2010 industrial value-added data with the formula:Here, I represents the industrial value added for each grid cell, and Ii represents the industrial value added for each country, EANTLi derived from ArcGIS statistical analysis and the regression allocation model.(2) Spatial Boundaries for Future Industrial Value Added: Using the Logistic-CA-Markov simulation principle and global land use data from 2010 and 2015 (from the European Space Agency), we simulated national land use changes for 2030 and 2050 and extracted urban land data as the spatial boundaries for future industrial value added. To comprehensively characterize the influence of different factors on land use and considering the research scale, we selected elevation, slope, population, GDP, distance to rivers, and distance to roads as land use driving factors. Accuracy validation using global 2015 land use data showed an average accuracy of 91.89%.(3) Estimation of Future Industrial Value Added: Based on machine learning and using the random forest model, we constructed spatialization models for industrial value added under different climate change scenarios: Here, tem represents temperature, prep represents precipitation, GDP represents national economic output, L represents urban land, D represents slope, and P represents population. The random forest model was constructed using factors such as 2010 industrial value added, urban land distribution, elevation, slope, distances to rivers, roads, railways (considering transportation), and settlements (considering noise and environmental pollution from industrial buildings), along with temperature and precipitation as climate scenario data. Except for varying temperature and precipitation values across scenarios, other variables remained constant. The model comprised 100 decision trees, with each iteration randomly selecting 90% of the samples for model construction and using the remaining 10% as test data, achieving a training sample accuracy of 0.94 and a test sample accuracy of 0.81.By analyzing the proportion of industrial value added to GDP (average from 2000 to 2020, data from the World Bank) and projected GDP under future Shared Socioeconomic Pathways (SSPs), we derived future industrial value added for each country under different SSP scenarios. Using these projections, we constructed regression models to allocate future industrial value added proportionally, resulting in spatial distribution data for 2030 and 2050 under different SSP scenarios.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports and Articles):Achievements: Developed a method for downscaling national-scale industrial value-added data by integrating DMSP/OLS nighttime light data, vegetation distribution, and other data. Published the global industrial value-added dataset.

Roughly ** percent of the annual GDP of lower income countries worldwide in 2050 could be at risk of loss due to exposure to climate hazards, in a slow transition scenario without adaptation measures. Extreme heat and water stress are forecast to have the biggest impact, at *** and *** percent, respectively. In contrast, in upper income countries, the same hazards would put less than one percent of the annual GDP at risk. Nevertheless, climate hazards would still put almost ***** percent of upper income countries' GDP at risk by 2050, in a no-adaptation scenario.

This dataset presents essential statistics related to global population dynamics. It includes information such as the year, economy, economy label, absolute population values in thousands, and urban population percentages. The dataset covers the period from 1950 to 2050, providing insights into population trends and urbanization patterns across various economies. The columns in data set is

Year Economy
Economy Label
Absolute value in thousands
Absolute value in thousands Missing value
Urban population as percentage of total population
Urban population as percentage of total population Missing value

Portugal PT: Population Projection: Mid Year: Growth data was reported at -0.630 % in 2050. This records a decrease from the previous number of -0.600 % for 2049. Portugal PT: Population Projection: Mid Year: Growth data is updated yearly, averaging -0.030 % from Jun 1991 (Median) to 2050, with 60 observations. The data reached an all-time high of 0.690 % in 1993 and a record low of -0.630 % in 2050. Portugal PT: Population Projection: Mid Year: Growth data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s Portugal – Table PT.US Census Bureau: Demographic Projection.

The ECHAM climate model has been developed from the ECMWF atmospheric model (therefore the first part of its name: EC) and a comprehensive parameterisation package developed at Hamburg therefore the abbreviation HAM) which allows the model to be used for climate simulations. The model is a spectral transform model with 19 atmospheric layers and the results used here derive from experiments performed with spatial resolution T42 (which approximates to about 2.8 degrees longitude/latitude resolution). The model has also been used at resolutions in the range T21 to T106. ECHAM4 is the current generation in the line of ECHAM models (Roeckner, et al., 1992). A summary of developments regarding model physics in ECHAM4 and a description of the simulated climate obtained with the uncoupled ECHAM4 model is given in Roeckner et al. (1996). The initial sea surface temperature and sea-ice data is the COLA/CAC AMIP SST and sea-ice data set. The mean terrain heights are computed from high resolution US Navy data set. The fraction of grid area covered by vegetation based on the Wilson and Henderson-Sellers (1985) data set. The ocean albedo is a function of solar zenith angle and the land albedo from the satellite data of Geleyn and Preuss (1983). A diurnal cycle and gravity wave-drag is included. The time-step of the model is 24 minutes, except for radiation which uses two hours. The ocean model is an updated version of the isopycnal model (OPYC3) developed by Josef Oberhuber (Oberhuber, 1993) at the Max-Planck-Institute for Meteorology, Hamburg, Germany. The name OPYC is derived from Ocean and isoPYCnal co-ordinates. The concept to use isopycnals as the vertical co-ordinate system for an OGCM is based on the observation that the interior ocean behaves as a rather conservative fluid. Even over long distances the origin of water masses can be traced back by considering the distribution of active or passive tracers. Treating the ocean as a conservative fluid fails in areas of significant turbulence activity such as the surface boundary layer. A surface mixed-layer is therefore coupled to the interior ocean in order to represent near-surface vertical mixing and to improve the response time-scales to atmospheric forcing which is controlled by the mixed-layer thickness. Since the model is designed for studies on large scales, a sea ice model with rheology is included and serves the purpose of de-coupling the ocean from extreme high-latitude winter conditions and promotes a realistic treatment of the salinity forcing due to melting or freezing sea ice. The experiments from which results are used here are the 1000-year unforced control simulation using the coupled ECHAM4/OPYC3 model and then two climate change simulations. The greenhouse gas only forced experiment (referred to as GGa1) used historical greenhouse gas forcing from 1860 to 1990 followed by a 1 per cent annum increase in radiative forcing from 1990 to 2099. The greenhouse gas and sulphate aerosol forced experiment (referred to as GSa1) used the GGa1 forcing, plus the negative forcing due to sulphate aerosols. This was represented by means of an increase in clear-sky surface albedo proportional to the local sulphate loading. The indirect effects of aerosols were not simulated. For 1860 to 1990 the historic sulphate aerosol forcing estimate was used and for 1990 to 2049 the aerosol forcing estimated for the IS92a emissions scenario. The GSa1 experiment did not extend beyond 2049. Fuller details of the ECHAM4/OPYC3 coupled model can befound at the DDC Yellow Pages.Several papers describe results using this version of the model - see Bacher et al. (1998), Oberhuber et al. (1998), Zhang et al. (1998). The climate sensitivity of ECHAM4 is about 2.6 degrees C.The A2 world consolidates into a series of roughly continental economic regions, emphasizing local cultural roots. In some regions, increased religious participation leads many to reject a materialist path and to focus attention on contributing to the local community. Elsewhere, the trend is towards ncreased investment in education and science and growth in economic productivity. Social and political structures diversify with some regions moving towards stronger welfare systems and reduced income inequality, while others move towards "lean" government. Environmental concerns are relatively weak, although some attention is paid to bringing local pollution under control and maintaining local environmental amenities.Like B1, the B2 world is one of increased concern for environmental and social sustainability, but the character of this world differs substantially. Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median projections. Income per capita grows at an intermediary rate to reach about US$12,000 by 2050. By 2100 the global economy might expand to reach some US$250 trillion. International income differences decrease, although not as rapidly as in scenarios of higher global convergence (A1, B1). Local inequity is reduced considerably through the development of stronger community support networks. Generally high educational levels promote both development and environmental protection. Indeed, environmental protection is one of the few remaining truly international priorities. However, strategies to address global environmental challenges are less successful than in B1, as governments have difficulty designing and implementing agreements that combine environmental protection with mutual economic benefits. The B2 storyline presents a particularly favorable climate for community initiative and social innovation, especially in view of high educational levels. Technological frontiers are pushed less than in A1 and B1 and innovations are also regionally more heterogeneous. Globally, investment in R and D continues its current declining trend, and mechanisms for international diffusion of technology and know-how remain weaker than in scenarios A1 and B1 (but higher than in scenario A2). Some regions with rapid economic development and limited natural resources place particular emphasis on technology development and bilateral co-operation. Technical change is therefore uneven. The energy intensity of GDP declines at about one percent per year, in line with the average historical experience of the last two centuries. Land-use management becomes better integrated at the local level in the B2 world. Urban and transport infrastructure is a particular focus of community innovation, contributing to a low level of car dependence and less urban sprawl. An emphasis on food self-reliance contributes to a shift in dietary patterns towards local products, with reduced meat consumption in countries with high population densities. Energy systems differ from region to region, depending on the availability of natural resources. The need to use energy and other resources more efficiently spurs the development of less carbon-intensive technology in some regions. Environment policy cooperation at the regional level leads to success in the management of some transboundary environmental problems, such as acidification due to SO2, especially to sustain regional self-reliance in agricultural production. Regional cooperation also results in lower emissions of NOx and VOCs, reducing the incidence of elevated tropospheric ozone levels. Although globally the energy system remains predominantly hydrocarbon-based to 2100, there is a gradual transition away from the current share of fossil resources in world energy supply, with a corresponding reduction in carbon intensity.

By 2050, Morocco could be among the world’s most dynamic emerging nations, Africa’s first climate-resilient economy, a regional leader in digital innovation, and a geopolitical bridge across three continents. This vision is not a distant dream; it is a trajectory already being shaped today. As global paradigms shift from climate upheaval to the AI revolution, from energy transitions to the fragmentation of traditional alliances, Morocco finds itself uniquely positioned to capitalise on its geographic, cultural, and strategic endowments.The stakes are high. What Morocco chooses to invest in over the next two decades will define its place in a reconfigured world order. This paper presents a forward-looking framework for policymakers, investors, civil society, and international partners seeking to contribute to and benefit from Morocco’s long-term transformation.Far more than a speculative blueprint, Morocco 2050 offers an integrated roadmap for sustainable growth, institutional resilience, and inclusive social progress. It invites its readers to envision Morocco not only as a gateway to Africa but as a model for balancing tradition and modernity, sovereignty and openness, continuity and innovation.Anchored in evidence and foresight, this report sets out a comprehensive strategy across sectors from renewable energy and digital economy to education, healthcare, logistics, and cultural industries. It explores the enablers of national renewal: good governance, adaptive policies, global partnerships, and the mobilisation of Morocco’s greatest asset, its people, both at home and across its diaspora.Morocco 2050 is a call to action. It is designed to guide decision-makers and attract visionary investors who understand that building the future is no longer optional; it is imperative. The time to shape Morocco’s success story is now.

This statistic indicates that range of variation in GDP, based on climate change impacts in 2050, broken down by region. It is predicted that in 2050 climate change impacts in the Middle East will lead to a decrease between * and ** percent of the region's GDP.

Algeria DZ: Number of Deaths data was reported at 421,380.000 Person in 2050. This records an increase from the previous number of 411,025.000 Person for 2049. Algeria DZ: Number of Deaths data is updated yearly, averaging 181,615.500 Person from Jun 1987 (Median) to 2050, with 64 observations. The data reached an all-time high of 421,380.000 Person in 2050 and a record low of 130,990.000 Person in 1990. Algeria DZ: Number of Deaths data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s Algeria – Table DZ.US Census Bureau: Demographic Projection.

United States Maximum 5-day Rainfall: 25-year Return Level data was reported at 9.986 mm in 2050. United States Maximum 5-day Rainfall: 25-year Return Level data is updated yearly, averaging 9.986 mm from Dec 2050 (Median) to 2050, with 1 observations. The data reached an all-time high of 9.986 mm in 2050 and a record low of 9.986 mm in 2050. United States Maximum 5-day Rainfall: 25-year Return Level data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Environmental: Climate Risk. A 25-year return level of the 5-day cumulative precipitation is the maximum precipitation sum over any 5-day period that can be expected once in an average 25-year period.;World Bank, Climate Change Knowledge Portal (https://climateknowledgeportal.worldbank.org);;

We provide free TelluBase data to select public data sources.These are small, but important, subsets of the full product.To start with, we offer all Latin American countries (except Venezuela) with GDP per city and subdivision in 2023 in these PDFs.If you represent an academic institution or a reputable media outlet and think you may benefit from TelluBase data, we may be able to provide it for free. Contact us with your query at info@tellusant.com.TelluBase covers 218 countries, 2600 cities, and 2500 subdivisions, 2000-2050. It gives a completely exhaustive view of the world economy.

Italy Maximum 5-day Rainfall: 25-year Return Level data was reported at 6.041 mm in 2050. Italy Maximum 5-day Rainfall: 25-year Return Level data is updated yearly, averaging 6.041 mm from Dec 2050 (Median) to 2050, with 1 observations. The data reached an all-time high of 6.041 mm in 2050 and a record low of 6.041 mm in 2050. Italy Maximum 5-day Rainfall: 25-year Return Level data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Environmental: Climate Risk. A 25-year return level of the 5-day cumulative precipitation is the maximum precipitation sum over any 5-day period that can be expected once in an average 25-year period.;World Bank, Climate Change Knowledge Portal (https://climateknowledgeportal.worldbank.org);;

Dominican Republic DO: Population Projection: Mid Year data was reported at 12,542,490.000 Person in 2050. This records an increase from the previous number of 12,508,980.000 Person for 2049. Dominican Republic DO: Population Projection: Mid Year data is updated yearly, averaging 8,231,374.000 Person from Jun 1950 (Median) to 2050, with 101 observations. The data reached an all-time high of 12,542,490.000 Person in 2050 and a record low of 2,352,968.000 Person in 1950. Dominican Republic DO: Population Projection: Mid Year data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s Dominican Republic – Table DO.US Census Bureau: Demographic Projection.

Peru has shown significant economic growth in the last two decades. Nevertheless, the progress in economic and social welfare has been made to the detriment of environmental performance. Peru committed to increase its mitigation efforts and seeks to achieve carbon neutrality in the economy by 2050, within the framework of the Paris Agreement. In this effort, the Carbon Neutrality Technical Study (MINAM & IDB, 2021) was undertaken. The study was supported by MINAM, IDB and the 2050 pathways platform and had the contribution of various public entities, the private sector and academia. The study estimated the benefits and costs of achieving carbon neutrality by 2050, providing important inputs to the process of updating the National Strategy Against Climate Change. The development of this CCDR report is aligned with the projections made in MINAM & IDB (2021), particularly with the transformations proposed for the Agriculture, forestry and land use (AFOLU) Sector. For the case of AFOLU and with relevance to the Amazon region a set of six were selected, these focus on three fundamental points: (i) reverse the trend of deforestation, addressing external pressures from related activities such as agriculture and livestock through agroforestry systems and silvopastures; (ii) increase the value generated by forestry operations, promoting the allocation of use rights in uncategorized lands as well as forest concessions under sustainable forest management; and (iii) assure that the sector becomes a carbon sink, through forest plantations for restoration and marketing.

From the IPCC website: The A1 Family storyline is a case of rapid and successful economic development, in which regional averages of income per capita converge - current distinctions between poor and rich countries eventually dissolve. In this scenario family, demographic and economic trends are closely linked, as affluence is correlated with long life and small families (low mortality and low fertility). Global population grows to some nine billion by 2050 and declines to about seven billion by 2100. Average age increases, with the needs of retired people met mainly through their accumulated savings in private pension systems. The global economy expands at an average annual rate of about three percent to 2100. This is approximately the same as average global growth since 1850, although the conditions that lead to a global economic in productivity and per capita incomes are unparalleled in history. Income per capita reaches about US$21,000 by 2050. While the high average level of income per capita contributes to a great improvement in the overall health and social conditions of the majority of people, this world is not without its problems. In particular, many communities could face some of the problems of social exclusion encountered by the wealthiest countries in the 20th century and in many places income growth could come with increased pressure on the global commons. Energy and mineral resources are abundant in this scenario family because of rapid technical progress, which both reduce the resources need to produce a given level of output and increases the economically recoverable reserves. Final energy intensity (energy use per unit of GDP) decreases at an average annual rate of 1.3 percent. With the rapid increase in income, dietary patterns shift initially significantly towards increased consumption of meat and dairy products, but may decrease subsequently with increasing emphasis on health of an aging society. High incomes also translate into high car ownership, sprawling suburbanization and dense transport networks, nationally and internationally. Land prices increase faster than income per capita. These factors along with high wages result in a considerable intensification of agriculture. Three scenario groups are considered in A1 scenario family reflecting the uncertainty in development of energy sources and conversion technologies in this rapidly changing world. Near-term investment decisions may introduce long-term irreversibilities into the market, with lock-in to one technological configuration or another. The A1B scenario group is based on a balanced mix of energy sources and has an intermediate level of CO2 emissions, but depending on the energy sources developed, emissions in the variants cover a very wide range. In the fossil-fuel intensive scenario group A1FI, emissions approach those of the A2 scenarios; conversely in scenario group A1T with low labor productivity or of rapid progress in "post-fossil" energy technologies, emissions are intermediate between those of B1 and B2. These scenario variants have been introduced into the A1 storyline because of its "high growth with high tech" nature, where differences in alternative technology developments translate into large differences in future GHG emission levels Ecological resilience is assumed to be high in this storyline. Environmental amenities are viewed in a utilitarian way, based on their influence on the formal economy. The concept of environmental quality might change in this storyline from "conservation" of nature to active "management" - and marketing - of natural and environmental services. Data are available for the following periods: 1961-1990, 2010-2039; 2040-2069; and 2090-2099 Mean monthly and change fields.

Jordan Maximum 5-day Rainfall: 25-year Return Level data was reported at 2.503 mm in 2050. Jordan Maximum 5-day Rainfall: 25-year Return Level data is updated yearly, averaging 2.503 mm from Dec 2050 (Median) to 2050, with 1 observations. The data reached an all-time high of 2.503 mm in 2050 and a record low of 2.503 mm in 2050. Jordan Maximum 5-day Rainfall: 25-year Return Level data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank.WDI: Environmental: Climate Risk. A 25-year return level of the 5-day cumulative precipitation is the maximum precipitation sum over any 5-day period that can be expected once in an average 25-year period.;World Bank, Climate Change Knowledge Portal (https://climateknowledgeportal.worldbank.org);;

Mexico Maximum 5-day Rainfall: 25-year Return Level data was reported at 11.868 mm in 2050. Mexico Maximum 5-day Rainfall: 25-year Return Level data is updated yearly, averaging 11.868 mm from Dec 2050 (Median) to 2050, with 1 observations. The data reached an all-time high of 11.868 mm in 2050 and a record low of 11.868 mm in 2050. Mexico Maximum 5-day Rainfall: 25-year Return Level data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Environmental: Climate Risk. A 25-year return level of the 5-day cumulative precipitation is the maximum precipitation sum over any 5-day period that can be expected once in an average 25-year period.;World Bank, Climate Change Knowledge Portal (https://climateknowledgeportal.worldbank.org);;