The EUR/USD exchange rate rose to 1.1708 on September 1, 2025, up 0.21% from the previous session. Over the past month, the Euro US Dollar Exchange Rate - EUR/USD has strengthened 1.07%, and is up by 5.79% over the last 12 months. Euro US Dollar Exchange Rate - EUR/USD - values, historical data, forecasts and news - updated on September of 2025.

The euro-to-dollar exchange rate fluctuated significantly in 2022, reaching its lowest recorded value since 2008 during that time. Figures were different later in the year, however, with a rate of 1.16 USD recorded at the end of August 27, 2025. The average (standardized) measure is based on the calculation of many observations throughout the period in question. It is therefore different from an annual measure at a point, which reflects concrete values as of end of the year.EstablishmentThe euro, which was established in 1992, introduced in non-physical form in 1999 and finally rolled out in 2002, is used by 19 of the 27 member states of the European Union. This group of countries is otherwise known as the eurozone or euro area. By 2018, the total value of euro currency in circulation was almost 1.2 trillion euros, or over 3.4 thousand euros per capita.Euro to USDBetween 2001 and 2008, the average annual exchange rate of the euro to the U.S. dollar noted a steep increase. In 2008, the euro to U.S. dollar annual average exchange rate was equal to 1.47, which meant that one euro could buy 1.47 U.S. dollars. By 2019, this value had decreased overall, to a value of 1.12 which meant that one euro could buy 1.12 U.S. dollars. Similar dynamics in the euro to U.S. dollar exchange rate were also reflected in the monthly exchange rate recently.

The EUR/USD exchange rate rose to 1.1715 on September 1, 2025, up 0.28% from the previous session. Over the past month, the Euro US Dollar Exchange Rate - EUR/USD has strengthened 1.13%, and is up by 5.86% over the last 12 months. Euro US Dollar Exchange Rate - EUR/USD - values, historical data, forecasts and news - updated on September of 2025.

Sweden NIER Forecast: Exchange Rate: Euro data was reported at 9.100 SEK/EUR in 2028. This stayed constant from the previous number of 9.100 SEK/EUR for 2027. Sweden NIER Forecast: Exchange Rate: Euro data is updated yearly, averaging 8.950 SEK/EUR from Dec 1971 (Median) to 2028, with 58 observations. The data reached an all-time high of 10.600 SEK/EUR in 2009 and a record low of 4.900 SEK/EUR in 1976. Sweden NIER Forecast: Exchange Rate: Euro data remains active status in CEIC and is reported by National Institute of Economic Research. The data is categorized under Global Database’s Sweden – Table SE.M020: Foreign Exchange Rate: Forecast: National Institute of Economic Research.

BMF Forecast: Foreign Exchange Rate: US Dollar: Annual Average data was reported at 1.100 USD/EUR in 2027. This stayed constant from the previous number of 1.100 USD/EUR for 2026. BMF Forecast: Foreign Exchange Rate: US Dollar: Annual Average data is updated yearly, averaging 1.100 USD/EUR from Dec 2016 (Median) to 2027, with 12 observations. The data reached an all-time high of 1.200 USD/EUR in 2022 and a record low of 1.100 USD/EUR in 2027. BMF Forecast: Foreign Exchange Rate: US Dollar: Annual Average data remains active status in CEIC and is reported by Federal Ministry of Finance. The data is categorized under Global Database’s Austria – Table AT.M007: Foreign Exchange Rate: US Dollar: Forecast.

The euro to Swiss franc exchange rate history suggests the currencies remained nearly unchanged since the summer of 2020. As of August 20, 2025, one euro was 0.94 Swiss francs. Between 2002 and 2007, the average annual exchange rate of the euro to the Swiss franc noted an increase. In 2007, for instance, one euro could buy over 1.6 Swiss francs. By 2021, this value had decreased to a value of roughly 1.1. Nevertheless, this was not the lowest point, as the exchange rate was its lowest in May 2015.

Luxembourg STATEC: Forecast: Foreign Exchange Rate: EUR/USD data was reported at 1.110 EUR/USD in 2021. This records an increase from the previous number of 1.090 EUR/USD for 2020. Luxembourg STATEC: Forecast: Foreign Exchange Rate: EUR/USD data is updated yearly, averaging 1.115 EUR/USD from Dec 2018 (Median) to 2021, with 4 observations. The data reached an all-time high of 1.230 EUR/USD in 2018 and a record low of 1.090 EUR/USD in 2020. Luxembourg STATEC: Forecast: Foreign Exchange Rate: EUR/USD data remains active status in CEIC and is reported by The Portal of Statistics of Luxembourg. The data is categorized under Global Database’s Luxembourg – Table LU.M009: Foreign Exchange Rate: Forecast: The Portal of Statistics of Luxembourg.

The Euro to Australian exchange rate history reveals a peak in March 2020 before declining well until mid-2022. Exchange rates fluctuate against each other constantly. As of August 20, 2025, the exchange rate was valued at 1.8 Australian dollars per euro. The rate at which one currency performs against another depends on the demand that it generates at any given time. Exchange rates are affected by several factors, including international trade, tourism, and geopolitical tensions.Euro gains strength against the Brexit PoundOne good example of geopolitical risks negatively impacting the strength of a currency is to look at the British Pound post Brexit referendum. The average annual exchange rate of the Euro to GBP increased significantly between 2015 and 2018.The Euro vs. the worldSince 2016, the euro has performed well against various other currencies. The Euro to U.S. dollar had seen its annual average exchange rate increase by 0.07 between 2016 and 2018, after slightly decreasing in 2019. Against the Indian Rupee, the Euro had performed even better, with the average annual exchange rate equaling 78.84 in 2019.

The official currency of Ireland is the Euro. Euro Exchange Rate - EUR/USD - Ireland - values, historical data, forecasts and news - updated on September of 2025.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Single prediction that uses

observations
prior information about the Earth-system
ECMWF's highest-resolution model

HRES Direct model output Products offers "High Frequency products"

4 forecast runs per day (00/06/12/18) (see dissemination schedule for details)
Hourly steps to step 90 for all four runs.

Not all post-processed Products are available at 06/18 runs or in hourly steps.

Prices for EURMUR Euro Mauritian Rupee including live quotes, historical charts and news. EURMUR Euro Mauritian Rupee was last updated by Trading Economics this September 2 of 2025.

ECB Projection: Foreign Exchange Rate: US Dollar data was reported at 1.140 EUR/USD in 2021. This stayed constant from the previous number of 1.140 EUR/USD for 2020. ECB Projection: Foreign Exchange Rate: US Dollar data is updated yearly, averaging 1.140 EUR/USD from Dec 2018 (Median) to 2021, with 4 observations. The data reached an all-time high of 1.180 EUR/USD in 2018 and a record low of 1.140 EUR/USD in 2021. ECB Projection: Foreign Exchange Rate: US Dollar data remains active status in CEIC and is reported by European Central Bank. The data is categorized under Global Database’s European Union – Table EU.M003: European Central Bank: Foreign Exchange Rate: Projection.

New Zealand ANZ Forecast: Foreign Exchange Rate: Euro: Annual data was reported at 0.540 NZD/EUR in 2025. This records a decrease from the previous number of 0.550 NZD/EUR for 2024. New Zealand ANZ Forecast: Foreign Exchange Rate: Euro: Annual data is updated yearly, averaging 0.570 NZD/EUR from Dec 2018 (Median) to 2025, with 8 observations. The data reached an all-time high of 0.600 NZD/EUR in 2021 and a record low of 0.540 NZD/EUR in 2025. New Zealand ANZ Forecast: Foreign Exchange Rate: Euro: Annual data remains active status in CEIC and is reported by ANZ Bank New Zealand. The data is categorized under Global Database’s New Zealand – Table NZ.M017: Foreign Exchange Rate: Forecast: ANZ Bank Ltd.

This dataset comprises past ensemble weather forecasts and reforecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). It contains surface-level weather variables relevant for energy modelling over the European continent. The data contains 50-member ensemble runs from ENS Extended (since 2012) and 20x10 member runs from the Reforecast (since 2016)

Collected from the ECMWF archive, the data spans potential weather scenarios equivalent to several thousand years. Due to the chaotic nature of weather, these ensemble forecasts, initiated from slightly perturbed snapshots, offer diverse predictions on potential weather outcomes, including accurate spatial correlations. The dataset aims to provide a richer base for modelling renewable energy systems, especially in scenarios beyond the standard 40-year weather record data, and facilitates the application of data-intensive machine learning techniques in energy modelling.

Only certain years have been fully collected.

Inflation Rate In the Euro Area remained unchanged at 2 percent in July. This dataset provides the latest reported value for - Euro Area Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Since July 2022, the BfG has been publishing a probability-based 6-week water level forecast for shipping-relevant levels on the Rhine. The 6-week forecast is provided for the Rhine gauges Maxau, Kaub, Cologne and Duisburg-Ruhrort via ELWIS, analogous to the 4-day low and medium water forecast. The 6-week forecast is updated twice a week (Tuesdays and Fridays), each in the late morning. The 6-week forecast is based on current measured water levels in the Rhine catchment area and information from over 1000 weather stations. The forecast is calculated, among other things, based on the ENS extended ensemble forecasts of the European Center for Medium-Range Weather Forecasts (ECMWF / ECMWF). Based on this extensive real-time data, the BfG uses a hydrological model to determine an ensemble of water level forecasts over the next 6 weeks. On this basis, in combination with statistical methods, probability distributions of weekly average values ​​for trend forecasting are determined at the above-mentioned Rhine gauges. This prediction is presented as boxplots. Further explanations can be found at https://www.bafg.de/DE/08_Ref/M2/04_Vorhersagen/6wRheinElbe/6w_node.html. The file name of the boxplot quantiles corresponds to the following scheme:

Since July 2022, the BfG has published a 6-week probability-based forecast of the water level for entertainment-relevant levels at the Elbe River. The 6-week forecast is provided for the Elbe-Pegel Dresden, Barby and Neu Darchau analogous to the deterministic low and medium water forecast via ELWIS. An update of the 6-week forecast takes place twice a week (Tuesdays and Fridays), each late morning. The 6-week forecast is based on current readings of the levels in the Elbe catchment area and the information from over 1000 weather stations. The forecast is calculated on the basis of the ENS extended ensemble forecasts of the European Centre for Medium-Term Weather Forecasts (ECMW/ECMWF). Based on this extensive real-time data, the BfG uses a hydrological model to determine an ensemble of water level forecasts over the next 6 weeks. On this basis, probability distributions of weekly average values for the trend forecast are determined in combination with statistical methods at the above-mentioned Elbe levels. This prediction is presented as box plots. Further explanations can be found at https://www.bafg.de/DE/08_Ref/M2/04_Vorhersagen/6wRheinElbe/6w_node.html. The file name of the boxplot quantile corresponds to the following schema:

Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics of the ocean-atmosphere system make the identification of the teleconnection signals difficult to be detected at a local scale as it could cause large uncertainties when using linear correlation analysis only. This paper explores the relationship between global SST and terrestrial precipitation with respect to long-term non-stationary teleconnection signals during 1981-2010 over three regions in North America and one in Central America. Empirical mode decomposition as well as wavelet analysis is utilized to extract the intrinsic trend and the dominant oscillation of the SST and precipitation time series in sequence. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant SST regions are extracted based on the correlation coefficient. With these characterized associations, individual contribution of these SST forcing regions linked to the related precipitation responses are further quantified through nonlinear modeling with the aid of extreme learning machine. Results indicate that the non-leading SST regions also contribute a salient portion to the terrestrial precipitation variability compared to some known leading SST regions. In some cases, these estimated contributions reveals some clues of the coupling interactions between oceanic and atmospheric processes. This dataset is associated with the following publication: Chang, N., S. Imen, K. Bai, and J. Yang. Multi-scale Quantitative Precipitation Forecasting Using Nonlinear and Nonstationary Teleconnection Signals and Artificial Neural Network Models. JOURNAL OF HYDROLOGY. Elsevier Science Ltd, New York, NY, USA, 548: 305-321, (2017).

This dataset series refers to the information on long-term fire weather forecast provided by the European Forest Fire Information System (EFFIS). ▷_How to cite: see below_◁

Long-term fire weather forecast attempts to provide useful information about the near-time climate that can be expected in the coming months. The seasonal forecast is not a weather forecast: weather can be considered as a snapshot of continually changing atmospheric conditions, whereas climate is better considered as the statistical summary of the weather events occurring in a longer time interval, up to several years. Near-time climate focuses on statistics about a given season instead of multiple years. In particular, long-term monthly forecast estimates temperature and rainfall anomalies that are expected to prevail over European and Mediterranean areas during the next four weeks (including the current week). Long-term seasonal forecast, instead, focuses on temperature and rainfall anomalies that are expected to prevail over European and Mediterranean areas during the next 7 months (including the current month). Despite the chaotic nature of the atmosphere, long term predictions are possible to some degree thanks to a number of components which themselves show variations on long time scales (seasons and years) and, to a certain extent, are predictable. The most important of these components is the ENSO (El Niño Southern Oscillation) cycle, which refers to the coherent, large-scale fluctuation of ocean temperatures, rainfall, atmospheric circulation, vertical motion and air pressure across the tropical Pacific. El Niño episodes (also called Pacific warm episodes) and La Niña episodes (also called Pacific cold episodes) represent opposite extremes of the ENSO cycle. The ENSO cycle is the largest known source of year-to-year climate variability.

Changes in Pacific sea surface temperature (SST) are not the only cause of predictable changes in the weather patterns. There are other causes of seasonal climate variability. Unusually warm or cold sea surface temperatures in the tropical Atlantic or Indian Ocean can cause major shifts in seasonal climate in nearby continents.

In addition to the tropical oceans, other factors that may influence seasonal climate are snow cover and soil wetness. All these factors affecting the atmospheric circulation constitute the basis of long-term predictions.

Overall, seasonal forecasting is justified by the long predictability of the oceanic circulation (of the order of several months) and by the fact that the variability in tropical SSTs has a significant global impact on the atmospheric circulation.

Seasonal forecasts provide a range of possible climate changes that are likely to occur in the season ahead. It is important to bear in mind that, because of the chaotic nature of the atmospheric circulation, it is not possible to predict the daily weather variations at a specific location months in advance. It is not even possible to predict exactly the average weather, such as the average temperature for a given month.

How to cite - When using these data, please cite the relevant data sources. A suggested citation is included in the following:

• San-Miguel-Ayanz, J., Houston Durrant, T., Boca, R., Libertà, G., Branco, A., de Rigo, D., Ferrari, D., Maianti, P., Artés Vivancos, T., Schulte, E., Loffler, P., Benchikha, A., Abbas, M., Humer, F., Konstantinov, V., Pešut, I., Petkoviček, S., Papageorgiou, K., Toumasis, I., Kütt, V., Kõiv, K., Ruuska, R., Anastasov, T., Timovska, M., Michaut, P., Joannelle, P., Lachmann, M., Pavlidou, K., Debreceni, P., Nagy, D., Nugent, C., Di Fonzo, M., Leisavnieks, E., Jaunķiķis, Z., Mitri, G., Repšienė, S., Assali, F., Mharzi Alaoui, H., Botnen, D., Piwnicki, J., Szczygieł, R., Janeira, M., Borges, A., Sbirnea, R., Mara, S., Eritsov, A., Longauerová, V., Jakša, J., Enriquez, E., Lopez, A., Sandahl, L., Reinhard, M., Conedera, M., Pezzatti, B., Dursun, K. T., Baltaci, U., Moffat, A., 2017. Forest fires in Europe, Middle East and North Africa 2016. Publications Office of the European Union, Luxembourg. ISBN:978-92-79-71292-0, https://doi.org/10.2760/17690

• San-Miguel-Ayanz, J., Schulte, E., Schmuck, G., Camia, A., 2013. The European Forest Fire Information System in the context of environmental policies of the European Union. Forest Policy and Economics 29, 19-25. https://doi.org/10.1016/j.forpol.2011.08.012

• San-Miguel-Ayanz, J., Schulte, E., Schmuck, G., Camia, A., Strobl, P., Libertà, G., Giovando, C., Boca, R., Sedano, F., Kempeneers, P., McInerney, D., Withmore, C., de Oliveira, S. S., Rodrigues, M., Houston Durrant, T., Corti, P., Oehler, F., Vilar, L., Amatulli, G., 2012. Comprehensive monitoring of wildfires in Europe: the European Forest Fire Information System (EFFIS). In: Tiefenbacher, J. (Ed.), Approaches to Managing Disaster - Assessing Hazards, Emergencies and Disaster Impacts. InTech, Ch. 5. http://doi.org/10.5772/28441