Accessing NOAA Climate Data Online

Fetch data from the National Oceanic and Atmospheric Administration Climate Data Online (NOAA CDO) < https://www.ncdc.noaa.gov/cdo-web/webservices/v2> API including daily, monthly, and yearly climate summaries, radar data, climatological averages, precipitation data, annual summaries, storm events, and agricultural meteorology.

Select and Download Climate Data from 'DWD' (German Weather Service)

Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see < https://www.dwd.de/EN/climate_environment/cdc/cdc_node_en.html> for more information). Choose observational time series from meteorological stations with 'selectDWD()'. Find raster data from radar and interpolation according to < https://brry.github.io/rdwd/raster-data.html>. Download (multiple) data sets with progress bars and no re-downloads through 'dataDWD()'. Read both tabular observational data and binary gridded datasets with 'readDWD()'.

Climate Indices

Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5).

Simulating Climate Data for Research and Modelling

Generate synthetic station-based monthly climate time-series including temperature and rainfall, export to Network Common Data Form (NetCDF), and provide visualization helpers for climate workflows. The approach is inspired by statistical weather generator concepts described in Wilks (1999) and Richardson (1981) .

PaleoPhyloGeographic Modeling of Climate Niches and Species Distributions

Reconstruction of paleoclimate niches using phylogenetic comparative methods and projection reconstructed niches onto paleoclimate maps. The user can specify various models of trait evolution or estimate the best fit model, include fossils, use one or multiple phylogenies for inference, and make animations of shifting suitable habitat through time. This model was first used in Lawing and Polly (2011), and further implemented in Lawing et al (2016) and Rivera et al (2020). Lawing and Polly (2011) "Pleistocene climate, phylogeny and climate envelope models: An integrative approach to better understand species' response to climate change" Lawing et al (2016) "Including fossils in phylogenetic climate reconstructions: A deep time perspective on the climatic niche evolution and diversification of spiny lizards (Sceloporus)" Rivera et al (2020) "Reconstructing historical shifts in suitable habitat of Sceloporus lineages using phylogenetic niche modelling.".

Non-Homogeneous Markov Switching Autoregressive Models

Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations. Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.

Plotting Functions for Climate Science and Services

A plotting package for climate science and services. Provides a set of functions for visualizing climate data, including maps, time series, scorecards and other diagnostics. Some functions are adapted and extended from the 's2dv' and 'CSTools' packages (Manubens et al. (2018) ; Pérez-Zanón et al. (2022) ), with more consistent and integrated functionalities.

Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections

These functions apply an analysis of variance to incomplete ensembles of climate projections. It provides estimates of climate change responses of all simulation chains and of all uncertainty variables. It has been applied to different ensembles of projections simulated to study the impact of climate change: for climate indicators in Evin et al. (2019) ; seasonal precipitation and temperature in Evin, Somot and Hingray (2021) ; hydrological variables in Evin et al. (2026) ; photovoltaic energy in Bichet et al. (2019) .

Simulating Climate Data for Research and Modelling

Advanced climate simulation, forecasting, visualization, export, and machine learning tools. Generates synthetic climate datasets for single or multiple weather stations using stochastic weather generation techniques. 'CDSimX' simulates daily climate variables including minimum and maximum temperature, rainfall, relative humidity, solar radiation, wind speed, wind direction, dew point temperature, and potential evapotranspiration. The package incorporates seasonal harmonic models, Markov chain rainfall occurrence processes, Gamma-distributed rainfall amounts, copula-based dependence structures, bias-correction procedures, and physical consistency constraints. 'CDSimX' supports climate data generation, environmental modeling, machine learning benchmarking, sensitivity analysis, and educational applications. Methods are based on established stochastic weather generation approaches described in Richardson (1981) , Wilks (1999) , and Osei et al. (2026) .

Integrating Phylogenetics and Climatic Niche Modeling

Implements some methods in phyloclimatic modeling: estimation of ancestral climatic niches, age-range-correlation, niche equivalency test and background-similarity test.