ERA5 Daily Weather Dataset

Updated 22 July 2025

ERA5 Daily Dataset is a globally consistent, multi-decadal weather reanalysis product providing daily summaries of atmospheric and surface variables from high-resolution hourly data.
Statistical and machine learning downscaling methods enhance ERA5 outputs, enabling precise site-specific climate analyses for flood risk, energy forecasting, and environmental modeling.
Innovations in data compression and real-time emulation make the dataset accessible and reliable, supporting robust climate trend analysis and advanced forecasting applications.

The ERA5 Daily Dataset is the ECMWF’s globally consistent, multi-decadal weather reanalysis product that provides daily values of a wide array of atmospheric and surface variables. Featuring a native horizontal resolution of 0.25° × 0.25° and coverage from 1940 onward, ERA5 daily aggregates are derived from the underlying hourly reanalysis, offering users gridded fields of temperature, precipitation, wind, radiation, and other variables with global reach. Its physically consistent, gap-filled fields—generated via 4D variational data assimilation—have made ERA5 an indispensable reference in climate science, hydrology, renewable energy, environmental modelling, and data-driven weather forecasting.

1. Dataset Structure, Variables, and Resolution

ERA5 provides global data at ~25–31 km spatial resolution and hourly temporal increments; the ERA5 Daily Dataset comprises daily summaries, such as means, minima, maxima, and, for some variables, accumulations. Variables include:

Air temperature (2 m, various pressure levels)
Precipitation (total and components)
Wind speed and direction (10 m, various heights)
Surface radiation (shortwave, longwave)
Humidity (surface and vertical)
Soil moisture, surface pressure, cloud cover, etc.

Daily values are routinely derived from aggregating the original hourly fields, maintaining consistency in climatology and extremes. For some applications (e.g., downscaling), paired hourly ERA5 and daily summaries are used for model input and validation (Karger et al., 2020, Glawion et al., 22 Nov 2024, Vandeskog et al., 2 Jul 2025).

2. Methodologies Leveraging ERA5 Daily Data

a. Statistical and Stochastic Downscaling

ERA5’s grid-based values often require statistical post-processing to generate site-specific or higher-resolution time series. Methods such as generalized additive models (GAM), regression splines, and stochastic weather generators are used to relate ERA5 daily predictors with local observations (Vandeskog et al., 2 Jul 2025). The core scheme is:

Fit a global GAM between ERA5 fields and observations across a network of stations.
Add local GAM adjustments per site to capture residual differences.
Model remaining autocorrelation through ARMA or similar models on probability-transformed (PIT) residuals.
For sites without observations, use donor sites’ trained models and generate ensembles for uncertainty quantification.

This multi-step approach substantially improves daily precipitation occurrence, intensity, and temperature distributions, especially where ERA5’s coarse grid might otherwise miss local variation.

b. Dynamical and Machine Learning–Based Downscaling

Neural network–based downscaling is increasingly prevalent. Conditional GANs and diffusion models are trained to enhance ERA5 daily (or hourly) precipitation and wind fields to higher spatial and/or temporal fidelity using paired target data (e.g., radar, high-res reanalyses) (Glawion et al., 22 Nov 2024, Merizzi et al., 27 Jan 2024). GAN discriminators enforce realism, while tailored losses (e.g., CRPS, Fraction Skill Score) ensure statistical and physical fidelity of extremes and local structures.

For instance, spateGAN-ERA5 generates global, 2 km/10 min precipitation ensembles conditioned on ERA5 input, matching both the mean and the spatial/temporal structure of intense rainfall (Glawion et al., 22 Nov 2024).

c. Extreme Value and Return Level Analysis

ERA5 daily precipitation products provide the foundation for statistical extreme value analyses over large domains. Regional frequency analysis (RFA) is often used, in which:

Grid points with similar scale-invariant tail properties are clustered into homogeneous regions (using the Partitioning Around Medoids algorithm).
Within clusters, a parsimonious extended generalized Pareto distribution is fitted to daily precipitation, with only scale allowed to vary locally.
This yields spatially smooth, cluster-consistent return level maps for planning and risk analysis (e.g., 10-, 50-, 100-year return periods) (Rivoire et al., 2021).

Such approaches exploit ERA5’s broad spatial-temporal coverage while controlling model complexity and reducing overfitting.

3. Validation and Benchmarking

ERA5 Daily Dataset is both a target for, and a benchmark in, validation studies across domains:

Renewable energy: Used as the input for PV and wind power yield simulations and as reference for statistical model evaluation (MBE, RMSE, Pearson’s r) (Camargo et al., 2020, Gruber et al., 2020).
Precipitation: Serve as coarse input to downscaling, with outputs validated against independent in situ or radar observations and other reanalyses (Karger et al., 2020, Cavalleri et al., 16 Jul 2024).
Wind: ERA5 wind fields are the calibration target for GCM/RCM evaluation and forecast skill assessments, with emphasis on capturing the distribution and extremes critical for power estimation (Morelli et al., 18 Nov 2024).
Simulation skill: Novel AI-driven models such as the UT-GraphCast Hindcast are trained to reproduce ERA5 states; their forecast accuracy (e.g., RMSE, anomaly correlation) is benchmarked against ERA5 analyses (Sudharsan et al., 20 Jun 2025).

ERA5’s role as a benchmark is supported by its spatial completeness and multi-decadal stationarity, yet limitations remain in resolving fine-scale or locally extreme events. Higher-resolution regional reanalyses, bias-correction using local observations, or stochastic and machine learning downscaling are frequently applied as enhancements.

4. Limitations and Biases

ERA5’s global span enables cross-region, multi-variable analyses, but several key limitations are consistently reported:

Resolution limits: Grid size (~25 km) smooths or underrepresents convective and localized extremes, leading to lower frequencies of high-impact events (e.g., rainfall >20 mm/day, tornado outbreaks) compared to higher-resolution products or local observations (Cavalleri et al., 16 Jul 2024, Karger et al., 2020).
Bias in specific variables: Noted temperature and cloud cover biases compared with in situ and satellite data (e.g., temperature −1.2 °C bias, cloud cover underestimation for observatory site assessment) (Priyatikanto et al., 16 Jun 2024). Similarly, ERA5 is observed to underestimate or misplace precipitation maxima in complex terrains.
Incomplete representation of physical phenomena: Some meteorological processes (e.g., local wind channeling, convective bursts) are inherently sub-grid at ERA5’s scale, necessitating post-processing, stochastic simulation, or high-res downscaling to restore variability (Vandeskog et al., 2 Jul 2025, Glawion et al., 22 Nov 2024).
Incompleteness in handling extreme value dependence: Data thinning or non-independence of daily extremes necessitates careful statistical treatment in return value and hazard mapping (Rivoire et al., 2021).

Users are frequently cautioned to complement ERA5 with regional products or advanced downscaling, and, where high-impact local extremes are concerned, to validate and calibrate with ground or radar data.

5. Practical Applications Across Research Domains

The ERA5 Daily Dataset underpins a wide array of applications:

Energy system modelling: ERA5’s solar radiation, temperature, and wind fields underpin simulations of PV and wind power yields, including bias correction and scenario evaluation (Camargo et al., 2020, Gruber et al., 2020).
Flood and risk modelling: Downscaled high-resolution precipitation fields derived from ERA5 daily products provide input for flood risk assessment, hydrological impact modelling, and infrastructure planning, exploiting both daily extremes and improved spatial accuracy (Karger et al., 2020, Glawion et al., 22 Nov 2024, Rivoire et al., 2021).
Climate trend analysis: ERA5’s stable multi-decadal record serves as a reference for climate variability, trend detection, and validation of climate model outputs (e.g., for multi-decadal wind, temperature, or precipitation changes) (Morelli et al., 18 Nov 2024, Cavalleri et al., 16 Jul 2024).
Weather forecasting and AI-based prediction: ERA5 is both a training target for deep learning weather forecasts (e.g., GraphCast, AFNO/FourCastNet) and a resource for analog forecasting, stochastic generators, and online emulation frameworks seeking to minimize data storage and transmission costs (e.g., CRA5 compressed datasets via VAEtransformers) (Cheon et al., 13 Feb 2024, Han et al., 6 May 2024, Tsoi et al., 6 Jan 2025, Song et al., 11 Oct 2024).
Environmental and socio-economic impact analysis: Aggregated or weighted ERA5 daily data, sometimes integrated with economic activity proxies (population density, night lights), is used for impact assessments at administrative-unit scale, with automated pipelines providing weighted averages, sums, or extremes for downstream modelling (Gortan et al., 2023).

6. Technical Innovations and Derivative Products

Numerous advances have emerged to make ERA5 more tractable and broadly accessible:

Data compression: The development of efficient codecs (e.g., VAEformer, leading to CRA5) allows compression ratios >300×, reducing storage from hundreds of terabytes to less than a terabyte, while preserving statistical skill for downstream forecasting and analysis (Han et al., 6 May 2024).
Real-time stochastic emulators: Online updating via Slepian bases and VAR models enables fast, memory-efficient generation of probabilistic wind speed fields, addressing computational bottlenecks and facilitating near real-time emulation with minimal data retention (Song et al., 11 Oct 2024).
Analogue and feature-based enrichment: Machine learning–driven analogue systems exploit autoencoder feature extraction on historical ERA5 fields to select analogues most relevant to new forecast cases, with demonstrated improvements in precipitation forecast accuracy (Tsoi et al., 6 Jan 2025).

These innovations expand ERA5’s usability for resource-constrained researchers and operational centres, without commensurate loss of analytical fidelity.

7. Ongoing Challenges and Directions

Despite broad adoption and technical progress, ERA5 Daily Dataset applications face important challenges:

In highly heterogeneous or coastal areas, bias-corrected or downscaled ERA5 products may exhibit reduced skill; further research into custom covariates, spatial co-modelling, and multivariate approaches is warranted (Vandeskog et al., 2 Jul 2025).
Integration of multi-modal and multi-source data through foundation models (e.g., EarthNet’s masked autoencoders) promises further gains in accuracy, especially for humidity and vertical structure, though some trade-off in air temperature accuracy relative to ERA5 must be managed (Vandal et al., 16 Jul 2024).
Ensuring data standardization, particularly in the context of renewable energy output simulations, remains a priority for data sharing and the enhancement of data-driven modelling workflows (Camargo et al., 2020).
For trend analysis, even ERA5 exhibits modest, non-negligible biases over long periods and in certain climatologically complex domains (e.g., wet bias in Alpine regions). Multiple verification and relaxation methods—wavelets, categorical scores, and neighborhood approaches—are encouraged for robust use (Cavalleri et al., 16 Jul 2024).

Broader access to ERA5 derivatives with adaptive compression, improved downscaling, and synergistic post-processing is likely to remain a major focus, with increased integration of AI models for downstream forecasting and impact analysis.

In sum, the ERA5 Daily Dataset represents a cornerstone resource in atmospheric and climate science, supporting diverse scientific and operational applications, extensive comparative validation, and ongoing methodological innovation across statistical, machine learning, and dynamical modelling paradigms. Its versatility continues to be enhanced through bias correction, stochastic/statistical downscaling, neural compression, and adaptive data assimilation methods, even as challenges of resolution, extreme event representation, and data management persist.