Systemic weather risk is a major obstacle for the formation of private (nonsubsidized) crop insurance. This paper explores the possibility of spatial diversification of insurance by estimating the joint occurrence of unfavorable weather conditions in different locations. For that purpose copula methods are employed that allow an adequate description of stochastic dependencies between multivariate random variables. The estimation procedure is applied to weather data in Germany. Our results indicate that indemnity payments based on temperature as well as on cumulative rainfall show strong stochastic dependence even at a national scale. Thus the possibility to reduce risk exposure by increasing the trading area of the insurance is limited. Irrespective of their economic implications our results pinpoint the necessity of a proper statistical modeling of the dependence structure of multivariate random variables. The usual approach of measuring stochastic dependence with linear correlation coefficients turned out to be questionable in the context of weather insurance as it may overestimate diversification effects considerably.